Artificial Intelligence Applications for Sustainable Technology Development

Thomas Hoppe

Artificial Intelligence Applications for Sustainable Technology Development

Author(s)	Thomas Hoppe
Country	Netherlands
Abstract	The increasing pressure on energy systems, industrial resources, urban infrastructure, and natural environments has intensified the need for technological development that combines operational advancement with environmental responsibility. Conventional approaches to sustainable technology often depend on fixed control mechanisms, historical planning models, and fragmented monitoring systems that may not adequately respond to rapidly changing operating conditions. Artificial Intelligence (AI) provides advanced computational capabilities for prediction, classification, optimization, anomaly detection, automated control, and adaptive decision support, creating substantial opportunities to strengthen sustainability-oriented technological systems. This study examines the applications of Artificial Intelligence in sustainable technology development through a conceptual and literature-based analytical approach. The analysis considers machine learning, neural networks, intelligent optimization, predictive analytics, and data-driven control within renewable energy systems, smart buildings, sustainable manufacturing, environmental monitoring, transportation, water management, and resource-efficient infrastructure. Particular attention is given to the conditions under which AI contributes to genuine sustainability outcomes rather than only improving operational productivity. The analytical findings indicate that AI can enhance sustainability by improving renewable-energy forecasting, reducing avoidable energy consumption, supporting predictive maintenance, detecting material and process inefficiencies, improving environmental monitoring, and enabling adaptive management of complex technological systems. AI also strengthens the capacity to identify relationships across large and heterogeneous datasets that are difficult to evaluate through conventional analytical methods. However, the sustainability value of AI is not automatic. High computational demand, poor data quality, inadequate interpretability, cybersecurity risks, infrastructure requirements, algorithmic bias, and insufficient environmental performance criteria may reduce or offset potential benefits. The study concludes that Artificial Intelligence should be considered an enabling capability within sustainable technology development rather than an independent sustainability solution. Effective implementation requires explicit environmental objectives, reliable data governance, lifecycle awareness, human oversight, transparent performance evaluation, and alignment between computational optimization and measurable sustainability outcomes.
Keywords	Artificial Intelligence, Sustainable Technology, Machine Learning, Resource Efficiency, Renewable Energy, Smart Systems, Environmental Sustainability.
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-01-31

View / Download PDF File

About AIJARD Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijard.com

American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Artificial Intelligence Applications for Sustainable Technology Development

Share this