Title: An IoT-driven enterprise energy efficiency optimisation model: a path analysis for promoting a green economy

Authors: Liang Zhao; Enhua Li; Xiuyun Zheng

Addresses: The Office of Human Resource, Cangzhou Normal University, Cangzhou 061000, Hebei, China ' School of Business, SEGI University, Kuala Lumpur 47810, Malaysia ' School of Business, SEGI University, Kuala Lumpur 47810, Malaysia

Abstract: Companies are reconsidering energy use for conservation. Path analysis provides an IoT-driven green economy enterprise energy efficiency optimisation model (IoT-E3OM) - real-time energy usage data from industrial, logistics, and facilities management IoT sensors and actuators. Multiple linear regression assesses process optimisation, equipment efficiency, behavioural interventions, and renewable energy integration, while path analysis examines causal relationships impacting energy efficiency. Energy management with environmental aims may enhance business efficiency and profitability. IoT-E3OM betters prior models in energy prediction (97.8%), efficiency (96.5%), accuracy (95.4%), energy consumption (10.2%) and mean absolute error (7.3%). This approach improves Sustainability through energy management and green economics.

Keywords: enterprise energy efficiency; optimisation; IoT; path analysis; green economy; multiple linear regression analysis; research hypothesis.

DOI: 10.1504/IJGW.2025.146661

International Journal of Global Warming, 2025 Vol.36 No.3, pp.311 - 324

Received: 17 Oct 2024
Accepted: 14 Mar 2025
Published online: 11 Jun 2025 *

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