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 preserve 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. Each 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 in fiscal years 2022 and 2024. The first row of each FY****_carbon_intensity_344meshes.csv file contains the mesh IDs corresponding to each area. Geographical coordinates for the meshes are provided in the respective meshcode_FY****.csv files, expressed in latitude and longitude based on the World Geodetic System. In this updated version (v2), the dataset has been expanded and refined as follows: The 2022 dataset has been updated (FY2022_carbon_intensity_344meshes.csv) based on the latest smart-meter data provided by the Secured Meter Data Sharing Association, which corrected part of the previously released dataset. A new dataset for fiscal year 2024 has been added (FY2024_carbon_intensity_344meshes.csv). As the available meshes slightly differ from those in 2022, the corresponding mesh list is included (meshcode_FY2024.csv). For both fiscal years, emission factors related to specific generation types (pumped-storage and biomass power) and interconnection power flows have been revised. 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