Library

@article{liu_how_2024,

title = {How AI powers ESG performance in China's digital frontier?},

author = {Xukang Liu and Chaoqun Ma and Yi-Shuai Ren},

url = {https://www.sciencedirect.com/science/article/pii/S1544612324013539},

doi = {10.1016/j.frl.2024.106324},

issn = {1544-6123},

year  = {2024},

date = {2024-12-01},

urldate = {2024-11-22},

journal = {Finance Research Letters},

volume = {70},

pages = {106324},

abstract = {This study investigates the effect of AI on corporate ESG performance using data from Chinese A-share listed firms from 2009–2022. The results indicate that AI enhances corporate R&D investment and the degree of digital transformation, therefore improving ESG performance. The driving effect of AI on ESG performance is more pronounced in firms located in provinces with greater digital financial inclusion. Our findings are robust. This study underscores the importance of AI development in enhancing corporate ESG performance, offering both theoretical support and practical recommendations for establishing a greener and sustainable economic system in China's digital frontier.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{li_using_2024,

title = {Using Artificial Intelligence in ESG Assurance},

author = {Nichole Li and Meehyun Kim and Jun Dai and Miklos A. Vasarhelyi},

url = {https://doi.org/10.2308/JETA-2022-054},

doi = {10.2308/JETA-2022-054},

issn = {1554-1908},

year  = {2024},

date = {2024-10-01},

urldate = {2024-11-22},

journal = {Journal of Emerging Technologies in Accounting},

volume = {21},

number = {2},

pages = {83–99},

abstract = {As environmental, social, and governance (ESG) reporting has become a mainstream channel for companies to communicate their commitment to sustainability issues, the need for reliable and transparent ESG reports is increasing. However, research on ESG assurance is still in its early stages. ESG assurance poses more challenges than traditional financial auditing due to the diverse subjects and types of information in ESG reports. This paper proposes using artificial intelligence (AI) technologies and exogenous data as solutions. It discusses how AI can enhance the efficiency and effectiveness of ESG assurance by assessing vast and extensive data. This paper also explores AI’s application throughout the general ESG assurance process and contributes to the discussion on providing high-quality ESG assurance services. Additionally, it provides practical implications for auditors, regulators, and stakeholders.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{chen_shock_2024,

title = {Shock or empowerment? Artificial intelligence technology and corporate ESG performance},

author = {Jia Chen and Ning Wang and Tongzhi Lin and Baoliu Liu and Jin Hu},

url = {https://www.sciencedirect.com/science/article/pii/S0313592624001930},

doi = {10.1016/j.eap.2024.08.004},

issn = {0313-5926},

year  = {2024},

date = {2024-09-01},

urldate = {2024-11-22},

journal = {Economic Analysis and Policy},

volume = {83},

pages = {1080–1096},

abstract = {Artificial intelligence (AI) plays a significant role in realizing sustainable economic development. This paper uses the textual content of annual reports of listed companies to count 73 words frequencies related to AI and construct AI indicators through precise vocabulary. It also examines how AI affects environment, social, and governance (ESG) performance at the firm level using unbalanced panel data of Chinese listed firms from 2007 to 2022. The results indicate that the development of artificial intelligence has significantly improved the ESG performance of Chinese listed companies, and the conclusion still holds after a series of robustness tests. As a moderating variable, macroeconomic policy uncertainty reinforces the positive impact of AI on ESG performance. In terms of the impact mechanism, AI enhances firms’ ESG performance by increasing firms’ total factor productivity and R&D expenditures. The results of heterogeneity analysis show that AI has a significant positive impact on the ESG performance of non-state-owned firms, firms with executives without overseas backgrounds, and technology and capital-intensive firms. Compared with the western region, AI in the eastern and central regions has a more significant improvement effect on ESG performance. Our study deepens the knowledge and understanding of the role played by AI in the green development process at the micro level. It provides valuable suggestions and reflections for promoting AI development at the micro-enterprise level.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{zhang_pathway_2024,

title = {The pathway to curb greenwashing in sustainable growth: The role of artificial intelligence},

author = {Dongyang Zhang},

url = {https://www.sciencedirect.com/science/article/pii/S0140988324002706},

doi = {10.1016/j.eneco.2024.107562},

issn = {0140-9883},

year  = {2024},

date = {2024-05-01},

urldate = {2024-11-22},

journal = {Energy Economics},

volume = {133},

pages = {107562},

abstract = {Artificial Intelligence (AI) can improve production efficiency and general quality of life through assisting human labor, potentially leading to the conversion of employment types, enhancing industrialization, and upgrading energy structure. This paper enriches the role of AI in improving sustainable growth by curbing hypocritical sustainable and greenwashing behaviors. By accessing the panel data from Chinese listed-firms for the period 2014–2021, we have shown that AI can significantly mitigate the existence of greenwashing behaviors by raising the disclosure quality of ESG rating scores. Moreover, the role of AI in mitigating greenwashing behaviors performs significantly in SOEs, less pollution-intensive industries, high environmental regulation and less developed green finance regions. Furthermore, the potential mechanisms of AI in mitigating greenwashing behaviors are displayed, including alleviating financial constraints, easing management cost, improving green innovations.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{lim_environmental_2024,

title = {Environmental, social, and governance (ESG) and artificial intelligence in finance: State-of-the-art and research takeaways},

author = {Tristan Lim},

url = {https://doi.org/10.1007/s10462-024-10708-3},

doi = {10.1007/s10462-024-10708-3},

issn = {1573-7462},

year  = {2024},

date = {2024-02-01},

urldate = {2024-11-22},

journal = {Artificial Intelligence Review},

volume = {57},

number = {4},

pages = {76},

abstract = {The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{sklavos_environmental_2024,

title = {Environmental, Social, and Governance-Based Artificial Intelligence Governance: Digitalizing Firms’ Leadership and Human Resources Management},

author = {George Sklavos and George Theodossiou and Zacharias Papanikolaou and Christos Karelakis and Konstantina Ragazou},

url = {https://www.mdpi.com/2071-1050/16/16/7154},

doi = {10.3390/su16167154},

issn = {2071-1050},

year  = {2024},

date = {2024-01-01},

urldate = {2024-11-22},

journal = {Sustainability},

volume = {16},

number = {16},

pages = {7154},

abstract = {The integration of artificial intelligence (AI) with environmental, social, and governance (ESG) factors is impacting the direction of enterprises and society in our swiftly expanding world. This collaboration has significant potential to tackle critical issues such as reducing the impact of climate change, fostering social integration, and improving corporate governance. Nevertheless, the implementation of AI gives rise to intricate matters and apprehensions, as it brings out a distinct array of hazards and ethical quandaries for ESG performance. The objective of the present research is to fill this gap by gathering and offering a contemporary evaluation of the influence of advancing technologies on the strategic leadership’s role in fulfilling the business goal within the context of ESG considerations. We used bibliometric analysis to investigate the study subject using R Studio version 4.2.0 and the bibliometric applications VOSviewer version 1.6.20 and Biblioshiny version 4.2.0. We obtained data from the Scopus database and used the PRISMA approach to suitably choose 205 research publications. The results suggest that it is essential to use AI and ESG to digitize the boardroom. Additionally, it is crucial to guarantee its security using an advanced detection system. Therefore, chief executive officers (CEOs) must give priority to the issues of transparency and cybersecurity to reduce risks and successfully inspire trust in business activities.},

note = {Number: 16 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{hao_artificial_2023,

title = {Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol},

author = {Xinyue Hao and Emrah Demir},

url = {https://www.emerald.com/insight/content/doi/10.1108/jm2-01-2023-0009/full/html},

doi = {10.1108/JM2-01-2023-0009},

issn = {1746-5664},

year  = {2023},

date = {2023-07-01},

urldate = {2024-11-22},

journal = {Journal of Modelling in Management},

volume = {19},

number = {2},

pages = {605–629},

abstract = {Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors.,Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain.,In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers.,Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias.,The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area.},

note = {Publisher: Emerald Publishing Limited},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{kazachenok_economic_2023,

title = {Economic and legal approaches to the humanization of FinTech in the economy of artificial intelligence through the integration of blockchain into ESG Finance},

author = {Olesya P. Kazachenok and Galina V. Stankevich and Natalia N. Chubaeva and Yuliya G. Tyurina},

url = {https://www.nature.com/articles/s41599-023-01652-8},

doi = {10.1057/s41599-023-01652-8},

issn = {2662-9992},

year  = {2023},

date = {2023-04-01},

urldate = {2024-11-22},

journal = {Humanities and Social Sciences Communications},

volume = {10},

number = {1},

pages = {1–9},

abstract = {The purpose of the article is to study the current experience and prospects of the humanization of FinTech in the economy of artificial intelligence. The research methodology is based on the use of the method of structural equation modeling (SEM). The study analyzes statistics for 2021–2022 (annual indicators). The sample included 118 countries. As a result, the modern international experience of FinTech humanization in the economy of artificial intelligence has been studied and the causal relationships of FinTech humanization in the economy of artificial intelligence through the integration of blockchain into ESG finance have been identified. The article proposes an economic and legal approach to the humanization of FinTech in the economy of artificial intelligence by integrating blockchain into ESG finance to ascertain the economic and political implications. The article contributes to the literature by clarifying the scientific provisions of the concept of the humanization of the economy. The theoretical significance of the obtained results is that the developed model (SEM) and the detailed regression equations have formed a comprehensive understanding of the patterns of humanization of FinTech. The resulting econometric model can be used to predict prospects for the development of blockchain-based ESG finance, as well as high-precision planning of state economic policy. The practical significance of the authors’ conclusions and recommendations is that they have formed a clear idea of modern barriers (“market failures” and “institutional traps”) and prospects (improvement of the institutional environment through the application of an economic and legal approach) to the humanization of FinTech in the economy of artificial intelligence through the integration of blockchain into ESG finance.},

note = {Publisher: Palgrave},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{saetra_ai_2023,

title = {The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities},

author = {Henrik Skaug Sætra},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sd.2438},

doi = {10.1002/sd.2438},

issn = {1099-1719},

year  = {2023},

date = {2023-01-01},

urldate = {2024-11-22},

journal = {Sustainable Development},

volume = {31},

number = {2},

pages = {1027–1037},

abstract = {AI and data are key strategic resources and enablers of the digital transition. Artificial Intelligence (AI) and data are also intimately related to a company's environment, social, and governance (ESG) performance and the generation of sustainability related impacts. These impacts are increasingly scrutinized by markets and other stakeholders, as ESG performance impacts both valuation and risk assessments. It impacts an entity's potential to contribute to good, but it also relates to risks concerning, for example, alignment with current and coming regulations and frameworks. There is currently limited information on and a lack of a unified approach to AI and ESG and a need for tools for systematically assessing and disclosing the ESG related impacts of AI and data capabilities. I here propose the AI ESG protocol, which is a flexible high-level tool for evaluating and disclosing such impacts, engendering increased awareness of impacts, better AI governance, and stakeholder communication.},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sd.2438},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{burnaev_practical_2023,

title = {Practical AI Cases for Solving ESG Challenges},

author = {Evgeny Burnaev and Evgeny Mironov and Aleksei Shpilman and Maxim Mironenko and Dmitry Katalevsky},

url = {https://www.mdpi.com/2071-1050/15/17/12731},

doi = {10.3390/su151712731},

issn = {2071-1050},

year  = {2023},

date = {2023-01-01},

urldate = {2024-11-22},

journal = {Sustainability},

volume = {15},

number = {17},

pages = {12731},

abstract = {Artificial intelligence (AI) is a rapidly advancing area of research that encompasses numerical methods to solve various prediction, optimization, and classification/clustering problems. Recently, AI tools were proposed to address the environmental, social, and governance (ESG) challenges associated with sustainable business development. While many publications discuss the potential of AI, few focus on practical cases in the three ESG domains altogether, and even fewer highlight the challenges that AI may pose in terms of ESG. The current paper fills this gap by reviewing practical AI applications with a main focus on IT and engineering implementations. The considered cases are based on almost one hundred publicly available research manuscripts and reports obtained via online search engines. This review involves the study of typical business and production problems associated with each ESG domain, gives background details on several selected cases (such as carbon neutrality, land management, and ESG scoring), and lists challenges that the smart algorithms can pose (such as fake news generation and increased electricity consumption). Overall, it is concluded that, while many practical cases already exist, AI in ESG is still very far away from reaching its full potential; however, one should always remember that AI itself can lead to some ESG risks.},

note = {Number: 17 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{saxena_technologies_2023,

title = {Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape},

author = {Archana Saxena and Rajesh Singh and Anita Gehlot and Shaik Vaseem Akram and Bhekisipho Twala and Aman Singh and Elisabeth Caro Montero and Neeraj Priyadarshi},

url = {https://www.mdpi.com/2071-1050/15/1/309},

doi = {10.3390/su15010309},

issn = {2071-1050},

year  = {2023},

date = {2023-01-01},

urldate = {2024-11-22},

journal = {Sustainability},

volume = {15},

number = {1},

pages = {309},

abstract = {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.},

note = {Number: 1 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{khoruzhy_esg_2022,

title = {ESG investing in the AI era: Features of developed and developing countries},

author = {Liudmila I. Khoruzhy and Alexander V. Semenov and Aleksandr V. Averin and Timur A. Mustafin},

url = {https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.951646/full},

doi = {10.3389/fenvs.2022.951646},

issn = {2296-665X},

year  = {2022},

date = {2022-10-01},

urldate = {2024-11-22},

journal = {Frontiers in Environmental Science},

volume = {10},

abstract = {IntroductionThe era of artificial intelligence (AI) is a period when automation for the first time goes beyond the centuries-old practice of production and covers a wide range of organizational processes in which intellectual support for managerial decision-making is provided (Dudukalov et al., 2021; Ivanov et al., 2022; Popkova, 2022). At the same time, the Sustainable Development Goals (SDGs) have become widespread in the global economic system. In their practical implementation, business practices are reviewed from the point of view of ESG principles and transformed in accordance with them. This process is called ESG investing, including environmental, social and governance investing on a systemic basis (Gao et al., 2021; Popkova et al., 2021; Popkova and Sergi, 2021; Rehman and Noman, 2022). ESG is a systemic approach to business management, which covers and reconsiders through the lens of the SDGs (orients toward their support) environmental (E: with the focus on corporate environmental responsibility), social (S: with the focus on corporate social responsibility) and corporate (G: with the focus on financial management, maximisation of profit and increase in economic effectiveness of business) management. Thus, ESG investment is a process of financing sustainable development (Aldowaish et al., 2022; Ge et al., 2022). The concept of “ESG performance” means that a company is evaluated (by shareholders and investors, government and society) by the ESG criterion during making decisions on management and interaction with the company (Inampudi and Macpherson, 2020). For this, corporate reporting is used – reports on sustainable development reports on corporate social and environmental responsibility, financial reports and ESG reports (Breedt et al., 2019). In the works of Fafaliou et al. (2022), Zhang et al. (2022) and Zhang et al. (2021), the scholars note a strong influence of ESG on companies in developed and developing countries: ESG determines the effectiveness of companies, their sustainability to economic crises, investment attractiveness and strategic perspectives for the development of business.In the age of AI, ESG practices are based on smart technologies – automatization means (robots, unmanned transport) under the control of AI, which are united in cyber-physical systems with the help of the Internet of Things (IoT). ESG investment in the age of AI acquires two specific features (Sætra, 2021; Selim, 2020). The first one is that ESG investments imply the financing of smart technologies, which systemically improve the environmental, social and financial characteristics of business activities (Minkkinen et al., 2022). The second one is that ESG investment is performed based on smart technologies, e.g., blockchain (Alkaraan et al., 2022). The AI era opens up wide opportunities for the development of ESG investments thanks to technological support for improving decision-making by all stakeholders (Teichmann et al., 2022). Investors get access to “smart” analytics of investment projects grouped, sorted and ranked according to the criterion of the degree of compliance with ESG principles, contribution to the implementation of the SDGs, as well as the correlation between risk and profitability (Aroul et al., 2022; Popkova et al., 2020; Popkova and Sergi, 2022). A business can establish stable AI communications with the external environment, attracting a larger volume of ESG investments and achieving greater payback (Shahzad et al., 2020). Intelligent and automated state-public monitoring of ESG investments is also becoming available to identify and encourage the most responsible market agents (Andersson et al., 2022).The established approach to the development of ESG investments in the AI era is focused on technology, and therefore it can be called technological. This approach is described in the works of Ielasi et al. (2020), Tong et al. (2022), and Yasmine and Kooli (2022) and involves stimulating scientific and technological progress for AI support of ESG investments. The disadvantage of the existing approach is that it does not take into account the possibility of using advanced “smart” technologies in practice, as well as the degree of use of their potential. As practice shows, the availability of advanced technologies is not enough for their application. Based on the technological approach, the works of Li et al. (2022), Minkkinen et al. (2022), Abdur Rahman Khan et al. (2022), and Sætra (2021) indicate the feasibility of developing "smart" technologies to increase the volume of ESG investments. Focusing on the existing concept of “smart” ESG investments, the AI economy is progressing, but despite the similar level of development of advanced “smart” technologies, there are serious differences in the intensity of automation of ESG investments in developed and developing countries (Jonsdottir et al., 2022). The available publications (Chen et al., 2022; Sharma et al., 2022) do not explain these differences, which is a gap in the literature. The set of conditions necessary and sufficient for the development of the AI economy is more fully reflected in UNCTAD (2022), which highlights: ICT ranking, showing the level of development, availability and quality of advanced telecommunications infrastructure required for the application of “smart” technologies; Skills ranking, demonstrating the availability of highly qualified personnel with digital competencies necessary for the use of “smart” technologies; R&D ranking, characterizing the direct accessibility of “smart” technologies and the degree of their innovation; Industry ranking, revealing the degree of high-tech production and international trade; Finance ranking, demonstrating the adequacy of funding allocations to “smart” technologies and flexibility of financial instruments to achieve this goal.Systematic consideration of the above conditions reveals the AI economy in a new light - from the standpoint of social institutes. Since it is the differences in institutional support that form the basis for categorizing the countries of the world with the division of developed and developing countries, this article, based on the works of Shkalenko et al. (2022), Yankovskaya et al. (2021), hypothesizes that the AI economy institutes to determine the features of ESG investments in developed and developing countries.The purpose of this article is to identify prospects and offer recommendations for the development of ESG investing in the AI era, taking into account the characteristics of developed and developing countries. To achieve this goal, the following tasks are set and solved: To identify the features of the impact of digital technologies of the AI era on the existing ESG investing practices in developed and developing countries; To identify the prospects for the development of ESG investing in the AI era, as well as to offer authors’ recommendations – separately for developed and developing countries.A theory for the relationship between ESG and AI: a literature review and gap analysisThe central scientific category of this paper is the age of artificial intelligence (AI). It is treated as a new, modern stage of the development of society and economy, in which smart technologies, which are based on AI, are widely applied in practice (Kukushkina et al., 2022; Ragulina et al., 2022). The age of AI began due to the Fourth Industrial Revolution, the essential difference of which from previous industrial revolutions is the systemic coverage of technological modernisation (Wilson et al., 2022). While in the past, industrial revolutions improved only production technologies, now – under the conditions of the Fourth Industrial Revolution – management technologies are also improved. AI ensures the intellectual support for decision-making, in particular investment decisions (Luitse and Denkena, 2021; Som, 2021). This article is based on the theory of the relationship between ESG and artificial intelligence (AI). ESG performance is the evaluation of the company’s effectiveness from the position of sustainable development with the systemic coverage of environmental (E: with the focus on corporate environmental responsibility), social (S: with the focus on corporate social responsibility) effectiveness and effectiveness of corporate governance (G: with the focus on profitability) (Avramov et al., 2022; Pedersen et al., 2021). ESG seriously influences companies in developed and developing countries (Cardillo et al., 2022; Chen et al., 2022; Lööf et al., 2022). Fritz-Morgenthal et al. (2022), Minkkinen et al. (2022), and Nauck (2019) point out in their works that AI allows (based on the Internet of Things, IoT) collecting and analyzing Big Data on the topic of green innovation and corporate social and environmental responsibility. This data can be used as a basis for drawing much more complete and reliable internal and external corporate sustainability reporting (Maas, 2018). In addition, smart reporting on the implementation of ESG investment projects can be drawn (Vetrò et al., 2019).Viriato (2019) and Wang et al. (2021) in their works hold that AI allows investors to make the most justified (coordinated) decisions on the location of ESG investments thanks to automated market analysis and intelligent support for decision-making. In particular, corporate reporting can be processed by AI at a rapid pace and serve as a milestone for investment decisions (Cornforth, 2018). Furthermore, AI allows building up the most efficient investment portfolios with a consistent risk-return ratio, as well as the ratio of economic, social and environmental efficiency of capital investment projects (Auer and Schuhmacher, 2016; In et al., 2019).Thus, the literature review has shown that both from the standpoint of demand (ESG investors) and the standpoint of supply (companies implementing ESG investment projects), AI contributes to benchmarking and ensures the balance of ESG investment markets. On the other hand, the gap analysis has revealed somewhat poor elaboration of the causal relationships of the development of ESG investments in the AI era, which is a gap in the literature. Since market patterns have their specific character in developed and developing countries, the causal relationships of the development of ESG investments in the AI era are studied and modelled individually in these categories of countries to fill the identified gap in this article.Materials and methodsThe initial point of this research is the following proposed hypothesis: institutes of the AI economy determine the specifics of ESG investment in developed and developing countries. The logic of the research consists in the following: at the qualitative level of the research, a potential connection between ESG analyses and principles of responsible AI is seen. This connection is manifested in the fact that responsible AI allows for high precision forecasting of the environmental consequences of the activities of companies and planning of the projects of corporate environmental responsibility (E). Responsible AI ensures the growth of corporate social responsibility through smart monitoring of the safety of workplaces, remote execution of manipulations that are dangerous for employees’ health, creation of knowledge-intensive jobs and expansion of opportunities for advanced training based on remote corporate training (S); responsible AI also stimulates the rationalisation of the use of resources, optimisation of all business processes, especially production and logistics, and increase in the scale effect and profitability of the business (G).The methodology of testing the hypothesis is based on regression analysis, as a reliable method of economic statistics. The research is performed in two successive stages. At the first stage, we determine the specifics of the influence of digital technologies of the age of AI on the existing practices of ESG investing in developed and developing countries. For this, we perform the economic and mathematical modelling of the influence of the factors of Readiness for frontier technologies according to the UNCTAD (2022) on ESG score according to Morningstar (2022). The research model has the following form: ESG=a+b1*ict+b2*skl+b3*r&d+b4*ind+b5*fin, (1)where ESG – ESG score, the rest – factors of readiness for frontier technologies:skl – Skills ranking;r&d – R&D ranking;ind – Industry ranking;fin – Finance ranking;ict – ICT ranking.To check the reliability of the econometric models, we use significance F and t-Stat. We formed a sample of developed countries (Denmark, Italy, the USA, Canada, Austria, Belgium, Australia, Norway, New Zealand and the Czech Republic) and a sample of developing countries (China, India, Colombia, Malaysia, Chile, Brazil, Indonesia, Qatar, Russia and Saudi Arabia). The samples contain countries with the highest level of development of ESG investment, according to the ranking by Morningstar (2022).At the second stage, we determine the prospects and offer recommendations for the development of ESG investment in the age of AI in developed and developing countries. For this, we use the least squares method based on the obtained regression models. We also use the method of SWOT analysis to perform a quantitative-qualitative study of strengths and weaknesses, opportunities and threats to the development of ESG investment in the age of AI in developed and developing countries.Features of the impact of digital technologies of the AI era on the existing ESG investing practices in developed and developing countriesTo determine the specifics of the development of ESG investments in the AI era in developed and developing countries (to test the hypothesis put forward in the article), an empirical study based on the available official statistics of Morningstar (2022), reflecting the level of development of ESG investments in various countries was conducted. Readiness for frontier technologies is also taken into account, reflecting the quantitative measurement of the level of development of the institutes of the AI economy. The study was conducted based on 10 developed and 10 developing countries from different parts of the world with the highest level of development of ESG investment in 2021 (Table 1).The benefit of the ranking of developing and developed countries shown in Table 1 is that it has revealed a significant discrepancy between the existing boundaries of these categories of countries based on the criterion of market freedom and the efficiency of institutes (the integration of countries into the OECD defines them as developed countries) as well as on the criterion of income level (according to the World Bank classification, countries with a high level of income are considered as developed countries, while all other countries are considered as developing countries) with the criteria of ESG investments and readiness for frontier technologies.For example, China is one of the most active users of artificial intelligence (AI) at the international scale, although it ranks among the developing countries. On the other hand, Austria is classified as a developed country by the OECD and the World Bank, while Table 1 shows that this country is classified as a country that is lagging behind in many respects. Standard boundaries of categories of countries have been stored in Table 1 to make the results comprehensible, easily interpretable, reproducible and comparable with other studies on the topic of distinctions between developed and developing countries.As a result of processing the data from Table 1 using regression and correlation analysis methods, the following two economic and mathematical models of the contribution of the institutes of the AI economy to the development of ESG investments were obtained: Model for developed countries: ESG=18.91-0.12ict+0.07skl-0.02r&d+0.11ind+0.13fin. The resulting model means that in developed countries, the development of ESG investments is positively influenced by such institutes of the AI economy as ICT and R&D. The cumulative correlation of ESG investments with the institutes of the AI economy is estimated at 83.86% (high). Because of the mentioned inconsistency of the sample of developed countries (reduced volume of ESG investments and readiness for frontier technologies despite the high-income level, high market freedom and high efficiency of institutes), the model for developed countries is only reliable at a significance level of 0.3 (Significance F=0 .27746), although the standard error is relatively small and is equal to 2.06. The t-statistics for the factor variables were as follows: for ICT ranking (ict): -1.04, for Skills ranking (skl): 0.73, for R&D ranking (r&d): -0.16, for Industry ranking (ind) : 1.38, for Finance ranking (fin): 1.76; Model for developing countries: ESG=32.86-0.07ict-0.01skl-0.07r&d+0.04ind+0.03fin. The resulting model means that in developed countries, the development of ESG investments is positively influenced by such institutes of the AI economy as ICT, Skills and R&D. The cumulative correlation of ESG investments with the institutes of the AI economy is estimated at 94.56% (very high). The model for developing countries proved to be more reliable – it is reliable at a significance level of 0.05 (Significance F = 0.0440), and the standard error is small and equals 1.22. The t-statistics for the factor variables were as follows: for ICT ranking (ict): -2.91, for Skills ranking (skl): -0.32, for R&D ranking (r&d): -2.73, for Industry ranking (ind): 4.49, for Finance ranking (fin): 1.91.Prospects and recommendations for the development of ESG investments in the AI era in developed and developing countriesTo determine the prospects for the development of ESG investments in the AI era in developed and developing countries, based on the obtained economic and mathematical models, it was found that in developed countries, due to the progress of ICT institutes (+94.76% compared to the level of 2021) and R&D (+95.15%), the level of development of ESG investments may increase up to 26.80 points, that is, by 10.55% compared to 2021 (24.24 points). To clarify the quantitative results, they were supplemented with a qualitative study using the SWOT analysis method, as a result of which (based on statistics from Table. 1) it was revealed that the strengths (S) of the AI economy in developed countries are the high level of development of such institutes as ICT (on average, in the sample of developed countries, 19.10 position) and R&D (20.60 position).The weaknesses (W) are the small contribution of skills to the development of ESG investments, despite the high level of development of this institute of the AI economy (14th place), as well as the moderate level of development and a small contribution to the development of ESG investments of such institutes as Industry (34th position) and Finance (25.30 position). Opportunities (O) are associated with the further development of ICT and R&D institutes, as well as with the transformation of Skills, Industry and Finance institutes towards greater support for the SDGs and achieving their more significant contribution to the development of ESG investments. Threats (T) consist in the slow pace of development of ICT and R&D institutes as well as difficulties in the transformation of the Skills institute.In developing countries, due to the progress of ICT institutes (+98.41%), Skills (+98.60%) and R&D (+97.01%), the level of development of ESG investment may increase up to 37.05 points, that is, by 22.57% compared to 2021 (30.22 points). The SWOT analysis showed that the strength (S) of the AI economy in developing countries is a moderate level of development of R&D institute (33.50 position). Weaknesses (W) are the low level of development of such institutes as ICT (62.70 position) and Skills (71.30 position), as well as a small contribution to the development of ESG investments in Industry and Finance institutes. Opportunities (O) are associated with the further development of R&D institute, with accelerated progress in the development of ICT and Skills institutes, as well as with the transformation of Industry and Finance institutes towards greater support for the SDGs and achieving their more significant contribution to the development of ESG investments. Threats (T) consist in the slow pace of development of the R&D institute, as well as in difficulties in the transformation of the Finance institute.DiscussionThe article contributed to the development of the concept of “smart” ESG investments by clarifying the causal relationships of the development of ESG investments in the AI economy. In contrast to Ielasi et al. (2020), Tong et al. (2022) and Yasmine and Kooli (2022), it has been proved that the contribution of the AI economy to the development of ESG investments is related not so much to institutes but technologies. This allows us to propose a new (alternative) approach to the development of ESG investments in the AI era, involving the systematic development of institutes of the AI economy. However, standard institutes (freedom of international trade, protection of investors, common "rules of the game" in industry markets) are not enough. Special institutes are required and are coming to the fore to ensure the movement of ESG investment flows in the AI era. Corporate social and environmental responsibility, green finance, corporate management, etc. serve as these special institutes. This is because ESG investments in the AI era, despite the traditional leadership of developed countries in most rankings, are more pronounced in developing countries.It is suggested that a new classification of countries, in which the boundaries of developed and developing countries will be determined with due account for the level of development of the mentioned institutes of ESG investments in the AI era, could serve as a basis for the proposed approach. This will provide a means for a more reliable definition of the positions of countries in the world economic system from the standpoint of environmental economics and management, as well as for a more objective and accurate assessment of their progress in the development of ESG investments in the AI era.The advantage of the new approach is, firstly, that it more accurately and reliably describes the regularities of development of ESG investments in the AI era. Secondly, the new approach explains the differences in the development of ESG investments in the AI economy of developed and developing countries, and also allows us to find unique application solutions for them, taking into account their specifics.Also, as a result of the study, unlike Li et al. (2022), Minkkinen et al. (2022), Rahman Khan et al. (2022) and Sætra (2021), it has been proved that the development of "smart" technologies alone is not enough to increase the volume of ESG investments - the development of institutes of the AI economy is also required. This served as an argument for a clear division of the AI economies of developed and developing countries, whose institutes contribute differently (among institutes and categories of countries) to the development of ESG investments.ConclusionThe result of the study is proof of the hypothesis put forward in the article. A review of international experience in 2021 showed that the institutes of the AI economy determine the features of ESG investments in developed (where ICT and R&D institutes are the most significant and highly developed) and developing countries (where ICT, Skills and R&D institutes are the most significant, but only R&D institute is moderately developed). The obtained models, reasonable prospects and proposed recommendations outlined the priorities for the development of institutes in the AI economy for the most effective support for ESG investments, taking into account the characteristics of each category of countries.The contribution of the article to the improvement of scientific knowledge consists in the fact that the article has provided a new basis for the classification of countries, which enables a more exact definition of their modern boundaries in the AI era. It is suggested that the level of development and institutes of ESG investments could serve as this new basis. The article has made its contribution to the literature through the development of the theory for the relationship between ESG and artificial intelligence (AI), showing that this relationship can be observed at the level of institutes, not technologies, as in the previous opinion.The theoretical significance of the results obtained in the article is related to the fact that the proposed new institutional approach to the development of ESG investments in the AI era takes into account the possibilities of using advanced “smart” technologies in practice, as well as the degree of use of their potential and therefore bridges the gap between theory and practice. The applied significance of the authors’ conclusions and recommendations is that they take into consideration the characteristics of developed and developing countries and allow for the most effective management of the AI economy (through the development of its target institutes proposed in the article) in support of the development of ESG investments.Recommendation and SolutionAs a prospective solution to the problem of the development of ESG investing in the age of AI, we propose a transition from the isolated development of technologies to the systemic development of institutes. Due to this, the process of development of ESG investing in the age of AI transforms from linear into cyclical, for institutes in society and the economy initiate the creation and implementation of new technologies, which, in its turn, strengthens the institutes and causes repetition of the cycle. In developed countries, for the development of ESG investment in the age of AI, it is recommended to focus on the development of the institute of ICT and institute of R&D, and in developing countries – also the institute of Skills. That is, technologies play an important role, and for this, it is recommended to improve the telecommunication infrastructure and disseminate ICT. However, the progress of technologies only is not enough in both categories of countries.A mandatory condition for the development of ESG investment in the age of AI is the growth of innovative activity in the economy. For this, it is recommended to increase the volume of financing of R&D and the share of science-intensive, high-tech and innovative products. In developing countries, an important role belongs to social adaptation. For this, it is recommended to fill in the gaps in competencies, train digital personnel, create knowledge-intensive jobs and implement corporate training.The advantage of the proposed solution is the comprehensive development of society, economy and technologies, as well as consideration of the specifics and offering of the authors’ applied recommendations for developed and developing countries. This will allow reaching the mass character of ESG investing and an increase in scale in the age of AI, as well as ensuring the reliable support for the implementation of the SDGs in the Decade of Action from society and business based on the technologies of the age of AI and ESG investing.Economic Policy ImplicationsThe results obtained allow concluding that the models of development of ESG investment in the age of AI are different in developed and developing countries. Based on the compiled econometric models and results of the SWOT analysis, the following recommendations for economic policy are offered for developed countries: 1) supporting the achieved high level of development of the institute of ICT and the institute of R&D through the improvement of the legal regulation of these institutes; 2) stimulating the growth of the contribution of skills to ESG investments through the development of the “knowledge economy”; 3) raising the level of the development of the institutes of industry and finance and increasing their contribution to ESG investments through the stimulation of support for the SDGs.The following recommendations for economic policy are offered for developing countries: 1) supporting the achieved high level of the development of the institute of R&D through further development of the innovative economy; 2) raising the level of the development of the institutes of ICT and skills through accelerated digital modernisation of society and economy; 3) stimulating the growth of the contribution to ESG investments by the institutes of industry and finance through stimulating corporate social responsibility. The proposed recommendations allow focusing the national economic policy on the key spheres, thus facilitating the increase in its effectiveness.Limitations and Prospects for Future ResearchSumming up the research, it should be noted that the institutional approach to studying the influence of the AI economy on ESG investments, which is offered and used in this paper, has demonstrated its high effectiveness. The advantage of the institutional approach is the possibility to combine qualitative and quantitative research methods, as well as to take into account the specifics of developed and developing countries.A limitation of this research is the generalised consideration of developed and developing countries at the level of categories, while in each country, institutes of the AI economy a specific, similarly to the practices of ESG investment. To deal with this limitation, it is recommended to perform a range of case studies, to identify the national models of development of ESG investment based on the institutes of the AI economy and with the use of the institutional approach.},

note = {Publisher: Frontiers},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}

@article{hughes_alternative_2021,

title = {Alternative ESG Ratings: How Technological Innovation Is Reshaping Sustainable Investment},

author = {Arthur Hughes and Michael A. Urban and Dariusz Wójcik},

url = {https://www.mdpi.com/2071-1050/13/6/3551},

doi = {10.3390/su13063551},

issn = {2071-1050},

year  = {2021},

date = {2021-01-01},

urldate = {2024-11-22},

journal = {Sustainability},

volume = {13},

number = {6},

pages = {3551},

abstract = {Environmental, Social and Governance (ESG) rating agencies have been instrumental in mainstreaming sustainability in the investment industry. Traditionally, they have relied on company disclosure and human analysis to produce their ratings. More recently however, technological innovation in data scraping and Artificial Intelligence (AI) have undercut the traditional approach. Tech-driven Alternative ESG ratings are becoming increasingly influential yet remain critically underexplored in sustainable finance scholarship. Grounded within financial geography and using mixed methods, this paper fills this gap by comparing a set of Traditional ratings, sourced from MSCI ESG, with an Alternative AI-based set of ESG ratings sourced from Truvalue Labs. Our results expand upon recent research on ESG ratings by shedding new light on low commensurability between Traditional and Alternative ESG ratings. Specifically, we show that differences in ratings are driven by four main factors: differences in ESG theorisation based on key issue selection, differences in data sources analysed, differences in weighting structures for rating aggregation, and finally differences in controversy analysis. Our findings are contextualised using participatory observations collected during fieldwork at a leading asset manager in the City of London. Overall, we show that the advantages of Alternative ESG ratings include higher levels of standardisation, a transparent ‘outside-in’ perspective on ratings, a more democratic aggregation process, and rigorous real-time analytics. We argue that these characteristics reflect a geographic reconfiguration of ESG rating construction, expanding from financial agglomerations to technological and digital spaces of innovation. While Alternative ESG ratings make major promises on how technology can reform sustainable investing, we recognise that risks remain.},

note = {Number: 6 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {AI and ESG},

pubstate = {published},

tppubtype = {article}

}