Topic: Systems Engineering Approaches for Carbon Footprint Reduction

This Special Issue explores the role of systems engineering approaches in carbon footprint reduction. Systems engineering has become a cornerstone of global efforts to reduce carbon footprints across sectors. The complexity of modern energy, manufacturing, and urban systems, together with the urgency of climate change mitigation, demands integrated, data-driven, and adaptive solutions. Systems engineering, with its holistic, lifecycle-oriented perspective, is uniquely positioned to coordinate technological, operational, and policy innovations for effective carbon management. Recent years have seen a surge in research and applications of advanced modeling, optimization techniques, digital integration, and policy frameworks, reflecting the multidisciplinary nature and growing relevance of systems engineering in addressing global sustainability challenges.

We welcome contributions on a range of systems engineering approaches, including advanced modeling techniques such as artificial intelligence/machine learning (AI/ML) and digital twins; integration of renewable energy technologies with hydrogen storage and electric vehicles within multi-energy systems; optimization methods employing metaheuristics and fuzzy Multi-Criteria Decision Making (MCDM) for sustainable societal and industrial applications; life cycle assessment (LCA) and optimization integrated with Building Information Modeling (BIM) and digital databases for early-stage and continuous evaluation; and adaptive policy and decision support frameworks combining system dynamics and agent-based models for robust carbon reduction strategies.

We particularly encourage submissions with the following outcomes: modeling techniques enabling real-time monitoring, predictive analytics, and scenario generation; renewable energy system integration leading to emissions reduction, cost savings, and enhanced grid flexibility; optimization methods that improve convergence performance, support robust trade-off analysis, and explicitly address uncertainty; LCA approaches that incorporate early hotspot detection, design optimization, and improved data quality; and policy-oriented studies that address “low-regret” policies, stakeholder engagement, and scenario planning.

Themes of interest include, but are not limited to:

1. Modeling Techniques: including AI/ML integration, digital twins,Model-Based Systems Engineering,and system dynamics; for example, SustainSIM, neural networks, ensemble ML, and the Internet of Things (IoT);

2. Renewable Systems Integration: including multi-energy systems, hydrogen storage, Electric Vehicles, and advanced Energy Management Systems; for example, hybrid energy storage, AI-driven Energy Management Systems, and sector coupling;

3. Optimization Methods: including metaheuristics, multi-objective Genetic Algorithms, MCDM, and hybrid algorithms; for example, Non-dominated Sorting Genetic Algorithm II, Particle Swarm Optimization, Ant Colony Optimization, fuzzy sets, and hybrid metaheuristics;

4. Lifecycle Assessment (LCA): including Digital integration, early-stage LCA, Ex-ante LCA, BIM, and sector databases; for example, BIM-LCA, digital workflows, and ML for embodied carbonanalysis;

5. Policy and Decision Support: including adaptive frameworks, robust decision making, and integrated Decision Support Systems; for example, Robust Decision Making, Adaptation Pathways, system dynamics, and agent-based models.

Systems engineering, carbon footprint reduction, AI/ML and digital twins, renewable energy systems integration, optimization methods, life cycle assessment (LCA), multi-energy systems, decision support and policy modeling