Lang Mai, Ming Liu, Han Hao, Xin Sun, Fanran Meng, Yong Geng, Zia Wadud, James E Tate, Zhenyu Dong, Haoyang Li, Jingxuan Geng, Hao Dou, Yunfeng Deng, Fanlong Bai, Zongwei Liu, Fuquan Zhao
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A high-resolution dataset on electric passenger vehicle characteristics in China and the European Union.
China and the EU are the world's largest Electric Vehicle (EV) markets, making it crucial to understand their electrification progress for global insights. However, previous assessments of regional EV markets often provide broad EV market characteristic estimations, but neglect critical spatial and segmental heterogeneity, thereby limiting research and policy precision. To fill such a knowledge gap, this study proposes a multi-dataset fusion approach that enables the characterization of passenger vehicle electrification progress in both China and the EU at highly resolved spatial, segmental, and powertrain levels for the year 2023. The dataset includes EV sales, market penetration, battery chemistry mix, and sales-weighted average battery capacity for all wheelbase-defined segments across 31 provinces and municipalities in China, as well as the EU27, Iceland, and Norway. It characterizes the current state of passenger vehicle electrification in China and the EU and supports further research on critical material demand estimation, decarbonization performance assessment, and related topics.
期刊介绍:
Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data.
The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.