Sustainable Environment

Artificial Intelligence for Climate Resilient and Low Carbon Infrastructure

Infrastructure and the built environment are increasingly exposed to climate hazards, resource constraints, rapid urbanization, and growing expectations for lower carbon development. At the same time, artificial intelligence, machine learning, digital twins, remote sensing, Internet of Things sensing, and data driven decision tools are creating new opportunities to understand, predict, and reduce environmental impacts across the infrastructure life cycle. This Article Collection will examine how advanced digital methods can support sustainable and climate resilient buildings, transport systems, utilities, and construction processes through better planning, design, operation, maintenance, and renewal. Particular attention will be given to approaches that connect digital innovation with measurable outcomes such as lower emissions, reduced material and energy use, longer asset life, improved adaptation, and more efficient use of public and natural resources.

The topic is important because infrastructure decisions made today will shape energy demand, greenhouse gas emissions, resource use, public safety, and climate vulnerability for decades. Conventional approaches often rely on fragmented data, static design assumptions, and reactive maintenance, limiting the ability to respond to changing environmental conditions and compound risks. Integrating AI with life cycle assessment, environmental monitoring, resilience analysis, and multi criteria decision making can help identify lower impact solutions, forecast deterioration and climate related risks, optimize maintenance and resource allocation, and support transparent evidence-based policy. However, digital methods also raise questions concerning uncertainty, explainability, data quality, transferability, and implementation. The Collection will therefore encourage rigorous studies that demonstrate both technical performance and credible environmental value.

Submissions are invited on the following subtopics:

• AI for environmental and infrastructure monitoring
• climate risk prediction and adaptation
• digital twins for sustainable buildings and infrastructure
• life cycle carbon and environmental assessment
• low carbon and circular construction
• sustainable transport and pavement systems
• energy and resource efficiency
• predictive maintenance under climate stress
• resilient water and utility networks
• remote sensing and computer vision for environmental assessment
• optimization and decision support for mitigation and adaptation
• smart materials and nature informed infrastructure solutions
• governance, policy, ethics, and implementation challenges for digital sustainability

Research articles, review articles, data notes, methods, registered reports, and brief reports are welcome. Interdisciplinary studies, comparative applications across climatic regions, field validated approaches, and research that translates digital innovation into actionable environmental outcomes are especially encouraged.

Keywords:

1. Artificial intelligence
2. Climate resilient infrastructure
3. Low carbon infrastructure
4. Digital twins
5. Life cycle assessment

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript. Sustainable Environment accepts the following types of article: Research Article, Review Article, Data Notes, Methods, Registered Report, Brief Report.

The deadline for submitting manuscripts is August 13, 2027.

Please contact Kara Roberts at kara.roberts@taylorandfrancis.com with any queries on discount codes regarding this Article Collection.

Please select Environmental Solutions from the list of available sections during submission. Failure to select the appropriate Article Collection or Section name can result in delays.

Guest Advisors declare no potential conflicts of interests in line with our Editorial Policies.

Dr. Odey Hasan Farouq Alshboul is an Associate Professor in the Department of Civil Engineering, Faculty of Engineering, The Hashemite University, Jordan. He earned his Ph.D. from the University of Central Florida in 2019 and specializes in project management, infrastructure asset management, artificial intelligence and machine learning, and sustainable and climate resilient infrastructure. His recent research addresses life cycle emissions, digital twins, sustainable pavements, infrastructure deterioration, climate resilience, and environmental risk prediction. His work integrates data driven methods with sustainability, resilience, and infrastructure decision making.

Dr. Shehadeh is an Associate Professor of Civil Engineering and academic leader with more than twelve years of experience in construction engineering and management, infrastructure sustainability, artificial intelligence, quality assurance, and research development. His work integrates machine learning, explainable artificial intelligence, optimization, reliability analysis, digital construction technologies, BIM, virtual reality, structural monitoring, climate-resilient infrastructure, and sustainable resource management. He develops practical, evidence-based methods that strengthen safety, productivity, quality, sustainability, and long-term infrastructure performance.