Applied IT & Engineering
Integrating Artificial Intelligence into Sustainability and ESG Accounting: Enhancing Environmental and Social Performance
Muslima Jahan Diba1*
Applied IT & Engineering 1 (1) 1-8 https://doi.org/10.25163/engineering.1110490
Submitted: 01 May 2023 Revised: 03 July 2023 Accepted: 08 July 2023 Published: 10 July 2023
Abstract
Background: Artificial intelligence (AI) is increasingly applied in sustainability and Environmental, Social, and Governance (ESG) accounting to enhance environmental and social performance. Traditional ESG reporting systems depend on manual operations which lead to operational inefficiencies and slow reporting and produce unreliable measurement results.
Methods: A cross-sectional survey of 310 U.S. based professionals, including ESG analysts, sustainability managers, and accountants, collected demographic data, AI adoption levels, organizational challenges, and recommended strategies for ESG enhancement. The research used descriptive statistics and χ² tests at p ≤ 0.05 and Pearson correlation to identify relationships between strategic approaches and ESG performance results.
Results: The primary conclusion is that training & skill development of employees (r = 1.00, p < 0.001, 85 respondents) is the most significant predictor of AI-based performance for ESG, followed by investment in AI systems (r = 0.92, p < 0.001, 90 respondents), and data governance & standardization (r = 0.85, p < 0.001, 75 respondents), while support from leadership (r = 0.19, p = 0.08, χ² = 2.45, 20 respondents) is of limited significance.
Conclusion: The findings from the Pearson correlation analysis indicated that employee training and skill development displayed strong positive correlations with the ESG outcomes and investment in AI systems, and the data governance and standardization. The findings provide suggests that continuous monitoring displays a statistically significant relationship with the outcome variable and support does not display any noteworthy association.
Keywords: Artificial Intelligence, AI Adoption, Sustainability Performance, Sustainable Development Goals, Corporate Governance
References
Alhogail, A., & Alsabih, A. (2021). Applying machine learning and natural language processing to detect phishing email. Computers & Security, 110, 102414. https://doi.org/10.1016/j.cose.2021.102414
Alkaraan, F., Albitar, K., Hussainey, K., & Venkatesh, V. (2021). Corporate transformation toward Industry 4.0 and financial performance: The influence of environmental, social, and governance (ESG). Technological Forecasting and Social Change, 175, 121423. https://doi.org/10.1016/j.techfore.2021.121423
Amel-Zadeh, A., & Serafeim, G. (2018). Why and How Investors Use ESG Information: Evidence from a Global Survey. Financial Analysts Journal, 74(3), 87–103. https://doi.org/10.2469/faj.v74.n3.2
Attar, R. K., & Komal, N. (2022). The emergence of Natural Language Processing (NLP) techniques in healthcare AI. In Springer eBooks (pp. 285–307). https://doi.org/10.1007/978-3-030-96569-3_14
Bonsón, E., & Bednárová, M. (2022). Artificial Intelligence Disclosures in Sustainability Reports: Towards an Artificial Intelligence Reporting Framework. In Lecture notes in information systems and organisation (pp. 391–407). https://doi.org/10.1007/978-3-030-94617-3_27
Canhoto, A. I., & Clear, F. (2019). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Business Horizons, 63(2), 183–193. https://doi.org/10.1016/j.bushor.2019.11.003
Cardoni, A., Kiseleva, E., & Lombardi, R. (2020). A sustainable governance model to prevent corporate corruption: Integrating anticorruption practices, corporate strategy and business processes. Business Strategy and the Environment, 29(3), 1173–1185. https://doi.org/10.1002/bse.2424
Crowston, K., Allen, E. E., & Heckman, R. (2011). Using natural language processing technology for qualitative data analysis. International Journal of Social Research Methodology, 15(6), 523–543. https://doi.org/10.1080/13645579.2011.625764
Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73–80. https://doi.org/10.1080/2573234x.2018.1543535
Del Carmen Díaz-Peña, L., Delgadillo, V. M. C., & Iván, C. M. (2022). Financial firm’s performance: a comparative analysis based on ESG metrics and net zero legislation. Journal of Sustainable Finance & Investment, 1–21. https://doi.org/10.1080/20430795.2022.2119830
Hawaj, A. Y. A., & Buallay, A. M. (2021). A worldwide sectorial analysis of sustainability reporting and its impact on firm performance. Journal of Sustainable Finance & Investment, 12(1), 62–86. https://doi.org/10.1080/20430795.2021.1903792
Hemanand, D., Mishra, N., Premalatha, G., Mavaluru, D., Vajpayee, A., Kushwaha, S., & Sahile, K. (2022). Applications of Intelligent Model to analyze the green finance for environmental development in the context of artificial intelligence. Computational Intelligence and Neuroscience, 2022, 1–8. https://doi.org/10.1155/2022/2977824
Kang, Y., Cai, Z., Tan, C., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139–172. https://doi.org/10.1080/23270012.2020.1756939
Karhade, A. V., Bongers, M. E., Groot, O. Q., Cha, T. D., Doorly, T. P., Fogel, H. A., Hershman, S. H., Tobert, D. G., Srivastava, S. D., Bono, C. M., Kang, J. D., Harris, M. B., & Schwab, J. H. (2020). Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery. The Spine Journal, 21(10), 1635–1642. https://doi.org/10.1016/j.spinee.2020.04.001
Kocmanová, A., & Šimberová, I. (2014). DETERMINATION OF ENVIRONMENTAL, SOCIAL AND CORPORATE GOVERNANCE INDICATORS: FRAMEWORK IN THE MEASUREMENT OF SUSTAINABLE PERFORMANCE. Journal of Business Economics and Management, 15(5), 1017–1033. https://doi.org/10.3846/16111699.2013.791637
Miglionico, A. (2022). The use of technology in corporate management and reporting of Climate-Related Risks. European Business Organization Law Review, 23(1), 125–141. https://doi.org/10.1007/s40804-021-00233-z
Milana, C., & Ashta, A. (2021). Artificial intelligence techniques in finance and financial markets: A survey of the literature. Strategic Change, 30(3), 189–209. https://doi.org/10.1002/jsc.2403
Nasrollahi, M., Fathi, M. R., Sanouni, H. R., Sobhani, S. M., & Behrooz, A. (2020). Impact of coercive and non-coercive environmental supply chain sustainability drivers on supply chain performance: mediation role of monitoring and collaboration. International Journal of Sustainable Engineering, 14(2), 98–106. https://doi.org/10.1080/19397038.2020.1853271
Nekhili, M., Boukadhaba, A., Nagati, H., & Chtioui, T. (2019). ESG performance and market value: the moderating role of employee board representation. The International Journal of Human Resource Management, 32(14), 3061–3087. https://doi.org/10.1080/09585192.2019.1629989
Nicoletti, B. (2021). Proposition of Value and Fintech Organizations in Banking 5.0. In Palgrave studies in financial services technology (pp. 91–152). https://doi.org/10.1007/978-3-030-75871-4_4
Patil, R. A., Ghisellini, P., & Ramakrishna, S. (2020). Towards Sustainable Business Strategies for a Circular Economy: Environmental, Social and Governance (ESG) performance and evaluation. In Springer eBooks (pp. 527–554). https://doi.org/10.1007/978-981-15-8510-4_26
Rajesh, R., & Rajendran, C. (2019). Relating Environmental, Social, and Governance scores and sustainability performances of firms: An empirical analysis. Business Strategy and the Environment, 29(3), 1247–1267. https://doi.org/10.1002/bse.2429
Sarker, I. H. (2022). AI-Based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2). https://doi.org/10.1007/s42979-022-01043-x
Sciarelli, M., Tani, M., Landi, G., & Turriziani, L. (2019). CSR perception and financial performance: Evidences from Italian and UK asset management companies. Corporate Social Responsibility and Environmental Management, 27(2), 841–851. https://doi.org/10.1002/csr.1848
Secinaro, S., Calandra, D., & Degregori, G. (2024). From Data to Disclosure: How modern technologies are transforming ESG reporting? In Lecture notes in networks and systems (pp. 77–87). https://doi.org/10.1007/978-981-99-8324-7_8
Simpson, S. N. Y., Aboagye-Otchere, F., & Ahadzie, R. (2021). Assurance of environmental, social and governance disclosures in a developing country: perspectives of regulators and quasi-regulators. Accounting Forum, 46(2), 109–133. https://doi.org/10.1080/01559982.2021.1927481
Svanberg, J., Ardeshiri, T., Samsten, I., Öhman, P., Neidermeyer, P. E., Rana, T., Semenova, N., & Danielson, M. (2022). Corporate governance performance ratings with machine learning. Intelligent Systems in Accounting Finance & Management, 29(1), 50–68. https://doi.org/10.1002/isaf.1505
Toniolo, K., Masiero, E., Massaro, M., & Bagnoli, C. (2020). Sustainable business models and Artificial intelligence: Opportunities and challenges. In Contributions to management science (pp. 103–117). https://doi.org/10.1007/978-3-030-40390-4_8
Trappey, A. J., Trappey, C. V., Wu, J., & Wang, J. W. (2019). Intelligent compilation of patent summaries using machine learning and natural language processing techniques. Advanced Engineering Informatics, 43, 101027. https://doi.org/10.1016/j.aei.2019.101027
Weng, W., Wagholikar, K. B., McCray, A. T., Szolovits, P., & Chueh, H. C. (2017). Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Medical Informatics and Decision Making, 17(1). https://doi.org/10.1186/s12911-017-0556-8
Zekos, G. I. (2021). AI Risk Management. In Springer eBooks (pp. 233–288). https://doi.org/10.1007/978-3-030-64254-9_6