Spatio-Temporal Graph Multi-Agent Modelling for Urban Carbon Flux in GeoAI-Enabled Digital Twins

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper introduces a novel integration of multi-agent reinforcement learning and graph neural networks for urban carbon modeling, offering a new paradigm for researchers in AI for sustainability.

🏢実務担当者:Corporate sustainability teams can leverage this model for more accurate carbon accounting at urban or district scale, improving net-zero planning and reporting.

🏛政策担当者:Urban policymakers can use this model to simulate the impact of carbon pricing and district heating policies before implementation, supporting evidence-based climate action.

Urban carbon flux estimation remains a critical challenge for net-zero planning, as conventional process-based models fail to capture the dynamic, multi-scale interactions between human decisions and physical systems. We propose the Spatio-Temporal Graph Multi-Agent Carbon Flux Model (STG-MACFM). This learning-driven framework replaces static emission inventories with a hierarchical agent architecture embedded within a GeoAI-enabled digital twin. The model decomposes the urban domain into numerous localised agents, each representing a building or neighbourhood unit. These agents maintain local states derived from real-time energy consumption, meteorological data, and building geometry, and they learn optimal intervention policies through proximal policy optimisation. A temporal attention mechanism within each agent captures delayed feedback loops, such as the thermal inertia of building materials or lagged occupant responses to policy changes. A global coordinator then constructs a dynamic urban topology graph using a graph attention network, where edge weights encode spatiotemporal dependencies like wind-driven CO₂ dispersion or shared grid constraints. This coordinator aggregates local states through a graph convolutional network to produce city-wide strategies, including carbon pricing rates or district heating setpoints. The global policy is optimised via a multi-agent deep deterministic policy gradient variant, with a reward function that penalises both total emissions and spatial inequity. The proposed model integrates seamlessly with existing data layers and simulation modules, replacing static emission factors with context-aware, real-time values. Furthermore, the closed-loop coupling between local agent behaviours and global strategic interventions enables the digital twin to simulate complex cross-scale socio-environmental interactions with high fidelity. This work introduces a novel paradigm for urban carbon modelling, where adaptive, graph-based coordination replaces rigid, top-down estimation, thereby offering a more realistic foundation for net-zero scenario optimisation.

• Zenodo https://zenodo.org/records/21531393first seen 2026-07-25 04:11:34