Leveraging machine learning for environmental cost management in green accounting

Abstract

This study explores the role of green technology investment, machine learning adoption, and data analytics capability in enhancing environmental cost efficiency (ECE), focusing on Asian companies. It investigates how these technological investments foster ecological innovation, which mediates the relationship between these factors and cost efficiency. Using a quantitative approach, data were collected from 330 companies across various Asian industries and analyzed using Structural Equation Modeling (SEM). The results show that green technology, machine learning, and data analytics significantly contribute to ECE, with environmental innovation as a critical mediator. Machine learning adoption and data analytics were found to have the most substantial impact on fostering innovation and driving cost savings. This study highlights the importance of integrating technology and innovation to achieve environmental sustainability and cost efficiency, offering valuable insights for Asian policymakers and business leaders. These findings contribute to the growing literature on sustainability and provide practical implications for businesses looking to enhance their competitiveness while reducing environmental impact.