AI-Enabled Net-Zero Cities: Innovations in Planning, Performance, and Resilience

Cities produce over 70% of global emissions. Global urbanization, coupled with the urgent need to mitigate climate change, has made the pursuit of “net-zero carbon cities” a core goal of sustainable development. Traditional urban systems - characterized by siloed infrastructure (e.g., energy, transportation, and waste management), static planning models, and limited real-time monitoring capabilities - face inherent limitations in achieving this goal. Conventional cities often suffer from inefficient resource allocation (e.g., uneven energy supply and demand), high carbon emissions from fossil fuel-dependent systems, and poor resilience to climate shocks (such as extreme temperatures, floods, and storms). Additionally, the lack of data integration and predictive analytics makes it difficult to optimize urban performance dynamically, leading to a widening gap between carbon reduction targets and practical implementation.

Achieving Net-Zero requires AI-driven transformation across planning, operations, and resilience. The rapid advancement of artificial intelligence (AI) technologies—including machine learning, big data analytics, digital twins, and IoT (Internet of Things) integration—has emerged as a transformative force to address these challenges. AI enables the integration of multi-dimensional urban data (e.g., energy consumption, traffic flow, and environmental sensors), supports dynamic and data-driven urban planning, optimizes the operation efficiency of infrastructure systems, and enhances the resilience of cities to climate risks. For instance, AI-powered demand forecasting can balance energy supply from renewable sources (e.g., solar and wind) with urban energy needs, while digital twin models can simulate and pre-empt the impact of extreme weather on transportation networks. Moreover, AI-driven lifecycle management of urban assets (from buildings to public transit) can reduce carbon footprints by extending service life and promoting circular resource use.

This Research Topic aims to showcase cutting-edge research on AI-enabled innovations for net-zero carbon cities, with a focus on three core dimensions: smart planning, performance optimization, and climate resilience. We seek to explore how AI can break down silos between urban systems, enable proactive rather than reactive urban management, and align technological solutions with social, economic, and environmental sustainability goals. Submissions may include experimental studies (e.g., AI pilot projects in real cities), simulation-based research (e.g., digital twin models for carbon accounting), or theoretical analyses (e.g., frameworks for AI-driven urban resilience). In particular, we prioritize research that addresses real-world urban challenges, including but not limited to renewable energy integration, low-carbon transportation, smart buildings, waste-to-energy systems, and climate-adaptive urban design.

We seek contributions that advance theory and practice across three pillars of Net-Zero urbanism:

Planning: Geo-AI, Generative Design, and Urban Digital Twins for scenario modeling and upstream decision support.

Performance: Real-time optimization of energy, buildings, and mobility using reinforcement learning, predictive analytics, and IoT orchestration.

Resilience: Climate-risk prediction, adaptive infrastructure, and recovery strategies rooted in longitudinal data and community knowledge.

We welcome submissions on (but not limited to):

• AI-driven urban planning and design for net-zero carbon goals;
• Symbol, Meaning, Shortcuts & How to Use
• AI-enabled performance optimization of urban infrastructure systems;
• Digital twins and AI for urban carbon accounting and monitoring;
• AI-enhanced urban resilience to climate change;
• AI and circular economy integration for net-zero cities;
• AI-driven forecasting, reinforcement learning control, microgrid optimization, and peer‑to‑peer energy trading
• Policy and governance innovations for AI-enabled net-zero cities.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review

Keywords: Artificial intelligence, Net-zero cities, Smart planning, Performance optimization, Climate resilience, Sustainable urban planning, Smart operation and maintenance

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.