Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)

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Harnessing Evolutionary Machine Learning for Net-Zero Construction: A Strategic Path to Sustainable Performance

Authors
Scott McDonald1, *, Thi Ngan Pham2, Huy Nguyen Huynh Gia3, Dinh Hoang Quan4, Khong Kim Anh5
1The Business School, RMIT International University, Ho Chi Minh City, Vietnam
2Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Vietnam
4Mamo Payments, Dubai, UAE
5Kühne Logistics University, Ho Chi Minh City, Vietnam
*Corresponding author. Email: scott.mcdonald@rmit.edu.vn
Corresponding Author
Scott McDonald
Available Online 6 April 2026.
DOI
10.2991/978-94-6239-624-1_14How to use a DOI?
Keywords
Evolutionary Machine Learning (EML); Net-Zero Construction; Sustainable Performance; Technology Adoption; Business Optimization First Section
Abstract

This study explores how Evolutionary Machine Learning (EML), an adaptive optimization approach within artificial intelligence, can drive the transition toward net-zero construction and sustainable business performance. Drawing on Ecological Modernization Theory, Adaptive Structuration Theory, and the Diffusion of Innovation framework, the research develops and empirically tests a strategic model explaining how EML-enabled technologies enhance both carbon neutrality and organizational outcomes. Using survey data from 213 Vietnamese construction firms, the findings reveal that the success of EML adoption depends on aligning technological integration with operational realities, stakeholder readiness, and long-term innovation strategies. EML is shown to optimize resource allocation, project scheduling, and carbon footprint management, providing firms with competitive and environmental advantages. The study contributes to sustainable digital transformation discourse by positioning EML as a practical tool for solving complex optimization challenges in developing economies, offering actionable insights for policymakers, practitioners, and researchers seeking to leverage intelligent systems for climate-resilient and high-performing construction supply chains.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
6 April 2026
ISBN
978-94-6239-624-1
ISSN
2352-5428
DOI
10.2991/978-94-6239-624-1_14How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Scott McDonald
AU  - Thi Ngan Pham
AU  - Huy Nguyen Huynh Gia
AU  - Dinh Hoang Quan
AU  - Khong Kim Anh
PY  - 2026
DA  - 2026/04/06
TI  - Harnessing Evolutionary Machine Learning for Net-Zero Construction: A Strategic Path to Sustainable Performance
BT  - Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)
PB  - Atlantis Press
SP  - 179
EP  - 190
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-624-1_14
DO  - 10.2991/978-94-6239-624-1_14
ID  - McDonald2026
ER  -