AI 中文总结
研究城市分布式能源资源下碳强度因子时空变化，提出计算该因子新方法及可转移代理模型，用有限参数准确估计，支持可持续城市运营，减少因忽略局部变化导致的排放估计误差。
AI 中文摘要
向可持续城市基础设施的转变促使分布式能源资源迅速增加，尤其是配备太阳能光伏装置的产消者。光伏系统高度依赖太阳辐射，导致局部碳强度因子存在显著时空变化。碳强度因子对优化能源消耗和减排至关重要。本文介绍一种在城市区域层面计算时空分辨碳强度因子的新方法。结果显示，即使在单个城市内，示例夏季中午这些因子范围为0至316克/千瓦时。还开发了一个可转移代理模型，仅使用与人口普查网格单元信息对齐的有限输入参数来估计这些因子。比较了传统基于决策树的方法和一种先进神经网络架构。结果表明代理模型能提供高精度估计，支持数据驱动的可持续城市运营，甚至对信息有限地区也有可转移性。案例研究进一步表明，忽略局部变化会导致示例建筑排放估计误差高达9%。
英文摘要
The transition toward sustainable urban infrastructure is driving a rapid increase in distributed energy resources, particularly among prosumers equipped with solar photovoltaic (PV) installations. Such PV systems are highly dependent on solar radiation, which fluctuates over time and across different city districts, leading to substantial spatial and temporal variations in local carbon intensity factors. The carbon intensity factor is a critical parameter for optimizing energy consumption and reducing emissions in context of smart building control, electric vehicle charging or district heating systems. Hence, high-resolution information on local carbon intensity factors is crucial for intelligent control strategies that minimize environmental impact across urban infrastructures. In this paper, we introduce a novel approach for calculating spatially and temporally resolved carbon intensity factors on an urban district level. The results show that, even within a single city, these factors range from 0 up to 316 g/kWh for an exemplary summer noon. We further develop a transferable surrogate model that estimates these factors using only a limited set of input parameters, typically available to municipalities or grid operators, aligned with census grid cell information. For the surrogate model we compare both traditional decision tree based methods and a state of the art neural network architecture. Our findings demonstrate that the surrogate model can provide highly accurate estimations, particularly in densely built urban areas, supporting data-driven sustainable city operations and indicating transferability even to regions with limited available information. A case study further reveals that neglecting local variations can lead to emission estimation errors of up to 9% for an exemplary building.
Journal ref2026 IEEE Conference on Technologies for Sustainability (SusTech)
DOI：10.1109/sustech67720.2026.11536190
