Deadline Date: 01 October 2026
The imperative for achieving carbon neutrality and fostering sustainable resource and environmental systems has catalyzed the integration of artificial intelligence (AI) and advanced computational technologies into modeling, optimization, and decision-making processes. AI-driven innovations—including machine learning, deep learning, agent-based modeling, system dynamics, and intelligent optimization algorithms—are increasingly pivotal in understanding complex resource-environment interactions, enhancing system efficiency, and facilitating the transition to a low-carbon future. This Special Issue invites high-quality contributions that explore AI-driven technological innovations in the context of resource and environmental system modeling, optimization, and carbon neutrality applications. We welcome submissions employing diverse methodological approaches, such as AI and machine learning, big data analytics, simulation modeling, optimization algorithms, digital twins, and other computational or empirical methods. Papers may address, but are not limited to, AI applications in energy systems, environmental monitoring, carbon emission forecasting, policy simulation, sustainable resource management, and cross-sectoral integration for carbon neutrality. The Special Issue aims to provide a comprehensive platform for advancing research and practice at the intersection of AI technology, system modeling, and carbon neutrality strategies, contributing to intelligent, resilient, and sustainable socio-environmental systems. Topics of interest include, but are not limited to: AI and Machine Learning for Environmental System Modeling Intelligent Optimization of Resource Allocation and Energy Systems Digital Twins and Simulation for Carbon Neutrality Scenarios Big Data Analytics in Environmental Monitoring and Forecasting AI-Driven Policy Simulation and Impact Assessment Sustainable Resource Management through Intelligent Systems Integration of Renewable Energy and Smart Grids with AI Technologies Climate Change Mitigation and Adaptation via Computational Innovations
Editorial board
