DOI: 10.5281/zenodo.14632413
title: Datasets for Deciphering Decarbonization Trajectories in China by Spatiotemporal-Accumulation Modeling of Electricity Carbon Footprint

description: Introduction to the Dataset

This dataset provides a comprehensive and dynamic assessment of the carbon footprint of electricity generation across China's provincial grids from 2020 to 2050. It is based on an innovative spatiotemporal-accumulation modeling framework that integrates dynamic material flow analysis (MFA) and life cycle assessment (LCA). The model captures the heterogeneous carbon footprints of low-carbon power infrastructure (LCPI), including wind turbines, photovoltaic (PV) panels, and lithium batteries, while accounting for their installation year and location.

Key Features of the Dataset:





High Spatiotemporal Resolution:The dataset offers annual carbon footprint factors for electricity generation at both national and provincial levels, enabling detailed analysis of regional decarbonization trajectories.




Dynamic LCPI Carbon Footprint Calculation:Unlike traditional methods that use static or averaged carbon footprint factors, this model dynamically tracks the carbon emissions associated with the production and operation of LCPI, ensuring a more accurate representation of their cumulative impact over time.




Scenario-Based Projections:The dataset includes projections under various scenarios, such as Shared Socioeconomic Pathways (SSPs), climate targets (e.g., 1.5°C and 2°C), and different carbon capture and storage (CCS) deployment rates. This allows users to explore the impacts of policy and technological advancements on decarbonization pathways.




Updated Baseline Parameters:The dataset incorporates officially released 2023 electricity carbon footprint factors for coal-fired power, gas-fired power, hydropower, nuclear power, wind power, photovoltaic power, solar thermal power, biomass power, and power transmission and distribution. These parameters ensure the model's alignment with the latest national standards and improve its reliability.




Improved Energy Storage Logic:The calculation logic for energy storage, including pumped hydro storage (PHS) and lithium battery storage, has been refined. The updated model considers energy storage impacts starting from 2020, ensuring smoother transitions in carbon footprint factors between years and avoiding abrupt changes.




Comprehensive System Boundaries:The model covers the entire lifecycle of electricity generation, from raw material extraction and infrastructure manufacturing to power generation and storage. It also accounts for the decarbonization of the grid and its synergistic effects with LCPI production.



Applications:





Academic Research: The dataset supports studies on energy transition, carbon neutrality, and climate policy, providing a robust foundation for analyzing decarbonization pathways.




Policy Development: Policymakers can use the dataset to design targeted strategies for reducing carbon emissions in the power sector and beyond.




Industrial Planning: Companies, especially those in energy-intensive industries, can leverage the dataset to assess the carbon footprint of their products and optimize production locations based on regional electricity carbon footprints.



Citation:

When using this dataset, please cite the following reference:Tang, J., Shan, R., Wang, P., Chen, W. Q., Gu, D., Li, G., ... & Lu, J. (2025). Deciphering Decarbonization Trajectories in China by Spatiotemporal-Accumulation Modeling of Electricity Carbon Footprint. iScience, 28(3), 111963.DOI: https://doi.org/10.1016/j.isci.2025.111963

Disclaimer:

This dataset and model are intended solely for academic research purposes and should not be used for actual carbon footprint assessment or certification. The predictions are based on hypothetical scenarios and do not represent the actual carbon footprint factors of any province in China for any given year. Users assume all risks and responsibilities associated with the use of this dataset.

For further inquiries or technical support, please contact the research team.