| Issue |
BIO Web Conf.
Volume 141, 2024
IX International Scientific Conference on Agricultural Science 2024 “Current State, Problems and Prospects for the Development of Agricultural Science” (AGRICULTURAL SCIENCE 2024)
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|
|---|---|---|
| Article Number | 04012 | |
| Number of page(s) | 11 | |
| Section | Agriculture and Agri-food Systems | |
| DOI | https://doi.org/10.1051/bioconf/202414104012 | |
| Published online | 21 November 2024 | |
Leveraging machine learning for environmental cost management in green accounting
1 De La Salle University, 2401 Taft Avenue, Manila, Metro Manila, Philippines
2 Hardrain Technologies LLC, Sheridan, Wyoming, USA
3 Rostov State Transport University, Rostov-on-Don 344038, Russia
* Corresponding author: wilson.cordova@dlsu.edu.ph
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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