Spatio-Temporal Dataset of Carbon Intensity in Utsunomiya City, Japan

This dataset presents the spatio-temporal distribution of CO₂ emission factors associated with electricity consumption in Utsunomiya City, Tochigi Prefecture, Japan. Carbon intensity was calculated using aggregated electricity consumption data at the 1-km mesh level, without referencing individual consumers, to consider data privacy. The estimates incorporate photovoltaic (PV) generation and behind-the-meter (BTM) self-consumption, along with the temporal variation in the grid electricity mix. The dataset provides lifecycle CO₂ emission factors (kg-CO₂/kWh) at a temporal resolution of 30 minutes and a spatial resolution of 1 km², covering 344 areas across the city for fiscal year 2022 (file: carbon_intensity_all_344mesh.csv). The first row of carbon_intensity_all_344mesh.csv contains the mesh IDs corresponding to each area. Geographical coordinates for each mesh are provided in meshcode.csv. Please note that the smart meter data used in this study was purchased from the Secured Meter Data Sharing Association specifically for this research and cannot be redistributed. By quantifying the intra-city variation in carbon intensity, this dataset aims to support applications such as the optimal allocation of distributed energy resources and the development of demand response strategies. Details of the carbon intensity calculation methodology are available in the following publication:Soma Sugano et al., Quantifying spatio-temporal carbon intensity within a city using large-scale smart meter data: Unveiling the impact of behind-the-meter generation, Applied Energy, Volume 383, 2025, DOI: 10.1016/j.apenergy.2025.125373本数据集呈现了日本栃木县宇都宫市与电力消费相关的二氧化碳排放因子的时空分布。为兼顾数据隐私，研究采用1公里网格级别的聚合电力消费数据计算碳强度（carbon intensity），未涉及单个用户信息。该估算纳入了光伏发电（photovoltaic (PV) generation）、表后自发自用（behind-the-meter (BTM) self-consumption）以及电网电力结构的时间变化特征。 本数据集提供了生命周期二氧化碳排放因子（单位：kg-CO₂/kWh），时间分辨率为30分钟，空间分辨率为1平方公里，覆盖该市344个区域，对应2022财年（数据文件：carbon_intensity_all_344mesh.csv）。该文件的第一行包含各区域对应的网格ID。每个网格的地理坐标已在meshcode.csv中提供。 请注意，本研究使用的智能电表数据购自安全电表数据共享协会（Secured Meter Data Sharing Association），仅供本研究使用，不得再次分发。 本数据集通过量化城市内部碳强度的时空差异，旨在支持分布式能源资源优化配置、需求响应策略制定等应用场景。碳强度计算方法的详细信息可参阅以下文献：Soma Sugano等，《利用大规模智能电表数据量化城市内部时空碳强度：揭示表后发电的影响》，《应用能源》（Applied Energy），第383卷，2025年，DOI: 10.1016/j.apenergy.2025.125373