期刊
COMPUTERS IN INDUSTRY
卷 141, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.compind.2022.103700
关键词
ESG reporting; Internet of Things; Blockchain; Data authenticity; Anomaly detection; Spatial-temporal analytics
资金
- Innovation and Technology Fund, Hong Kong [ITP/021/20LP]
- Guangdong Basic and Applied Basic Research Foundation, P.R. China [2022A1515010232]
This study develops an environmental smart reporting system based on blockchain and IoT technologies to address the authenticity and credibility issues in ESG disclosures. Methods such as unsupervised neural network and self-organizing map are used for anomaly detection and data authenticity assessment. Experimental simulation demonstrates the effectiveness of the proposed system and methods.
Environment, social and governance (ESG) disclosures required on listed companies have aroused considerable interest in both academia and industry for sustainable development and investing. However, the authenticity and credibility of the ESG report exposed to the public remain in doubt due to black box-like reporting processes with massive human involvements. In this study, a framework of environmental smart reporting system (BI-ESRS) based on the blockchain and Internet of Things (IoT) technologies is developed to automate the acquisition of environment-related data and make the reporting reliable and traceable. In addition, we evaluate the authenticity of the data collected from IoT devices, considering human-made counterfeits on measuring instruments for greenwashing. It is anticipated to stimulate companies to submit high-quality data without fake. Specifically, an unsupervised neural network-enabled spatial-temporal analytics (UN-STA) method is devised to achieve anomalies detection and index the data with an authenticity rate. An artificial neural network, self-organizing map (SOM), is applied to construct the prediction model. The received signal strength indicator (RSSI) of Bluetooth low energy (BLE), the vibration amplitude of smart instruments and data uploading interval constitute the input vector for competitive learning. Finally, an experimental simulation is carried out to demonstrate the implementation of the proposed system and method, and their effectiveness has also been testified. Moreover, the sensitivity of the SOM model over the three factors has been analyzed by applying the control variate technique. This work is expected to serve as a reference for practitioners to satisfy similar requirements in the industry and inspire new ideas for scholars.0 2022 Elsevier B.V. All rights reserved.
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