Abstract
The Internet of Things (IoT) has the potential to significantly impact Environmental, Social, and Governance (ESG) outcomes. By automating and optimizing processes and systems, IoT can help improve energy efficiency, conserve resources, and reduce pollution. It can also have social impacts, such as changing the nature of work and raising concerns about data privacy. Additionally, the governance of IoT raises important ethical and regulatory considerations. In order to ensure that the adoption of IoT contributes positively to ESG outcomes, it is important to carefully consider the potential unintended consequences and to develop and deploy the technology in a responsible and sustainable manner.
In this paper, we propose a framework based on SAS and Microsoft Azure technologies to acquire real time data from appliances, define a logic block to determine the range of data and devices to be monitored, and trigger real time alarms when needed. As the adoption of IoT continues to grow, it will be important to monitor and evaluate its impacts on ESG, and to identify and implement best practices for ensuring that IoT can contribute positively to environmental, social, and governance outcomes.
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References
Agarwal, P.: Smart urban traffic management system using energy efficient optimized path discovery. In: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 858–863 (2023)
AkkaÅŸ, M.A.: Healthcare and patient monitoring using IoT. Internet Things 11, 100173 (2020)
Antoncic, M.: Uncovering hidden signals for sustainable investing using big data: artificial intelligence, machine learning and natural language processing. J. Risk Manag. Financ. Inst. 2(13), 106–113 (2020)
Chen, H.M.: The impact of wearable devices on the construction safety of building workers: a systematic review. Sustainability 15(14), 11165 (2023)
Daugaard, D.: Emerging new themes in environmental, social and governance investing: a systematic literature review. Account. Finan. 60(2), 1501–1530 (2020)
Dewitte, S.C.: Artificial intelligence revolutionises weather forecast, climate monitoring and decadal prediction. Remote Sens. 16(13), 3209 (2021)
Ding, S.T.: Opportunities and risks of internet of things (IoT) technologies for circular business models: A literature review. J. Environ. Manag. 336, 117662 (2023)
Dwivedi, D., Batra, S., Pathak, Y.K.: A machine learning based approach to identify key drivers for improving corporate’s ESG ratings. J. Law Sustain. Dev. 11(1), e0242 (2023). https://doi.org/10.37497/sdgs.v11i1.242
Dwivedi, D.N., Tadoori, G., Batra, S.: Impact of women leadership and ESG ratings and in organizations: a time series segmentation study. Acad. Strateg. Manag. J. 22(S3), 1–6 (2023)
Farjana, M.F.: An IoT-and cloud-based e-waste management system for resource reclamation with a data-driven decision-making process. IoT 4(3), 202–220 (2023)
Jebur, T.K.: Greening the internet of things: a comprehensive review of sustainable IoT solutions from an educational perspective. Indones. J. Educ. Res. Technol. 3(3), 247–256 (2023)
Krambia-Kapardis, M.S.: Ethical leadership as a prerequisite for sustainable development, sustainable finance, and ESG reporting. In: Dion, M. (ed.) Sustainable Finance and Financial Crime, pp. 107–126. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28752-7_6
Mhlanga, D.: Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review. Energies 16(2), 745 (2023)
Moudgil, V.H.: Integration of IoT in building energy infrastructure: a critical review on challenges and solutions. Renew. Sustain. Energy Rev. 174, 113121 (2023)
Nitlarp, T.: The impact factors of industry 4.0 on ESG in the energy sector. Sustainability 15(14), 9198 (2022)
Nyenno, I.T.: Managerial future of the artificial intelligence. Virtual Econ. 6(2), 72–88 (2023)
Rathore, B.M.: An exploratory study on role of artificial intelligence in overcoming biases to promote diversity and inclusion practices. In: Impact of Artificial Intelligence on Organizational Transformation, pp. 147–164 (2022)
Sætra, H.S.: A framework for evaluating and disclosing the ESG related impacts of AI with the SDGs. Sustainability 13, 8503 (2021)
Salman, M.Y.: Review on environmental aspects in smart city concept: water, waste, air pollution and transportation smart applications using IoT techniques. Sustain. Cities Soc. 104–567 (2023)
Saxena, A.S.: Technologies empowered environmental, social, and governance (ESG): an industry 4.0 landscape. Sustainability 1(15) (2022)
Soori, M.A.: Internet of things for smart factories in industry 4.0, a review. Internet Things Cyber-Phys. Syst. (2023)
Tedeschi, S.E.: A design approach to IoT endpoint security for production machinery monitoring. Sensors 10(19) (2019)
Tomáš Hák, S.J.: Sustainable development goals: a need for relevant indicators. Ecol. Ind. 60, 565–573 (2016)
Toorajipour, R.S.: Artificial intelligence in supply chain management: a systematic literature review. J. Bus. Res. (122), 502–517 (2021)
Wu, W., Fu, Y.: Consortium blockchain-enabled smart ESG reporting platform with token-based incentives for corporate crowdsensing. Comput. Ind. Eng. 172, 108456 (2022)
World Economic Forum. Internet of Things, Guidelines for Sustainability. World Economic Forum (2018)
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Pozzi, F.A., Dwivedi, D. (2023). ESG and IoT: Ensuring Sustainability and Social Responsibility in the Digital Age. In: Tiwari, S., Ortiz-RodrÃguez, F., Mishra, S., Vakaj, E., Kotecha, K. (eds) Artificial Intelligence: Towards Sustainable Intelligence. AI4S 2023. Communications in Computer and Information Science, vol 1907. Springer, Cham. https://doi.org/10.1007/978-3-031-47997-7_2
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