Reducing Greenhouse Gas Emissions Using Digital Twin Technology: A Sustainable Systems Approach

Addressing climate change necessitates innovative and data-driven approaches for monitoring, managing, and reducing greenhouse gas (GHG) emissions across various sectors. Digital Twin Technology (DTT)—which creates dynamic, real-time digital replicas of physical systems— emerges as a transformative tool in this context. This work explores the potential of DTT to accelerate emission reduction efforts in key domains including energy, manufacturing, transportation, and urban infrastructure. By enabling real-time monitoring, predictive maintenance, and system-level optimization, DTT enhances operational efficiency while minimizing environmental impact. The study presents a comprehensive review of current literature, proposes a machine learning–based methodology for forecasting emissions, and introduces a robust evaluation framework. Case studies from recent implementations are analyzed to demonstrate the tangible benefits and scalability of DTT in achieving decarbonization goals. Findings confirm that Digital Twin Technology plays a critical role in advancing toward net-zero emissions and fostering long-term sustainability.