AI-Driven ESG Compliance Monitoring in Smart Infrastructure Project's

References

[1] Alsaigh, A., Shrestha, R. and Khan, A., 2022. Artificial intelligence for ESG reporting: Opportunities and challenges in the construction sector. Journal of Building Engineering, 47, p.103874.

[2] Antle, R. and Baber, W.R., 2003. Auditing accounting estimates: A review of the recent academic literature. Auditing: A Journal of Practice & Theory, 22(1), pp.69–85.

[3] Binns, R., Veale, M., Van Kleek, M. and Shadbolt, N., 2018. 'It's reducing a human being to a percentage': Perceptions of justice in algorithmic decisions. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp.1–14.

[4] Candelon, F., Reeves, M., Swartz, S. and van Liebergen, B., 2021. AI and ESG: How artificial intelligence can accelerate environmental, social, and governance goals. Boston Consulting Group [online]. Available at: https://www.bcg.com

[5] Di Vaio, A., Palladino, R., Pezzi, A. and Kalisz, D.E., 2020. The role of digital innovation in knowledge management systems: A systematic literature review. Journal of Business Research, 123, pp.220–231.

[6] Guenoun, M., Ahmed, M.D., Ballesty, S. and Ismail, N., 2022. Towards explainable AI (XAI) in ESG data management for infrastructure development. International Journal of Construction Management, 1–15. https://doi.org/10.1080/15623599.2022.2033084

[7] Heikkurinen, P., Rinkinen, S. and Wilén, K., 2019. Urban sustainability and artificial intelligence: Automation for people?. Futures, 115, p.102481.

[8] Liang, Y., Wang, Y., Jiang, H. and Liang, L., 2022. Explainable artificial intelligence for ESG disclosure evaluation: A machine learning approach. Expert Systems with Applications, 195, p.116640.

[9] Li, Y., Zhang, X. and Ma, Z., 2020. Leveraging artificial intelligence for sustainable development: A review of the ESG landscape. Journal of Cleaner Production, 276, p.123234.

[10] Mora, L., Deakin, M. and Reid, A., 2019. Strategic principles for smart city development: A multiple case study analysis of European best practices. Technological Forecasting and Social Change, 142, pp.70–97.

[11] Niknejad, N., Ismail, W., Bahari, M. and Nor, K.M., 2021. A systematic literature review on the application of machine learning and blockchain technologies in smart cities. Sustainable Cities and Society, 67, p.102715.

[12] Pizzi, S., Caputo, A., Corvino, A. and Venturelli, A., 2020. Management research and corporate social responsibility: A bibliometric investigation of an evolving intellectual territory. Business Ethics: A European Review, 29(3), pp.356–375.

[13] Siew, R.Y.J., Balatbat, M.C.A. and Carmichael, D.G., 2016. The relationship between sustainability practices and financial performance: Evidence from the Australian property industry. Sustainability Accounting, Management and Policy Journal, 7(2), pp.204–228.

[14] Wang, Z., Zhu, Q. and Geng, Y., 2013. Stakeholders' influence on corporate environmental strategy in manufacturing industry. Journal of Cleaner Production, 47, pp.248–257.

[15] Yu, D., Li, B. and Zhang, L., 2018. ESG performance and firm value: Evidence from Chinese publicly listed companies. Sustainability, 10(12), p.4694

@article{1710073,
      author = {Carlos Umoru},
      title = {AI-Driven ESG Compliance Monitoring in Smart Infrastructure Project's},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {993-998},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710073.pdf},
      abstract = {The increasing intricacy and regulatory scrutiny regarding Environmental, Social, and Governance (ESG) policies within smart infrastructure projects have escalated the need for Artificial Intelligence (AI) technologies to improve monitoring, auditing, and responsibility at all levels. This research aims to examine the specific AI technologies of Natural Language Processing (NLP), machine learning, and real-time anomaly detection to determine their impact on enabling infrastructure development that is compliant with ESG policies and is efficient, scalable, and transparent. This research synthesizes theoretical and empirical literature from 2000 to 2022 to determine the impact of AI on ESG document classification, environmental monitoring, and automation of governance frameworks in large infrastructure projects. Most respondents noted that the application of AI in ESG frameworks significantly aids the detection of ESG risks, decreases compliance cost, and enhances the consistency of reporting throughout the project life cycle. Among the challenges AI faces in implementing ESG policies are data heterogeneity, algorithmic bias, lack of defined ESG and AI regulatory frameworks, and lack of defined governance frameworks. This research were pointed out the importance of setting uniform criteria for ESG data standards, compliance frameworks, human-in-the-loop governance, and capacity-building initiatives for ESG and AI professionals. These findings underscore the critical role of AI in bridging technological innovation with sustainable and ethical infrastructure development.},
      keywords = {Smart Infrastructure, Capacity Building, AI regulatory, environmental monitoring},
      month = {May},
  }