中国和欧盟电动乘用车特征的高分辨率数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
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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引用次数: 0

摘要

中国和欧盟是世界上最大的电动汽车(EV)市场,因此了解它们的电气化进展对于全球洞察至关重要。然而,以往对区域电动汽车市场的评估往往提供了广泛的电动汽车市场特征估计,但忽视了关键的空间和分段异质性,从而限制了研究和政策的准确性。为了填补这一知识空白,本研究提出了一种多数据集融合方法,使中国和欧盟的乘用车电气化进展能够在高度分辨率的空间、分段和动力总成水平上表征2023年。该数据集包括中国31个省市、欧盟27国、冰岛和挪威的电动汽车销量、市场渗透率、电池化学成分组合以及所有轴距定义细分市场的销售加权平均电池容量。它描述了中国和欧盟乘用车电气化的现状,并支持对关键材料需求估计、脱碳性能评估和相关主题的进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A high-resolution dataset on electric passenger vehicle characteristics in China and the European Union.

A high-resolution dataset on electric passenger vehicle characteristics in China and the European Union.

A high-resolution dataset on electric passenger vehicle characteristics in China and the European Union.

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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
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.
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