Analysis of Global Trade Competition Pattern of New Energy Vehicles and Fuel Vehicles based on Multi-Layer Network

Abstract— Use the 2023 global trade export data of new energy vehicles and fuel vehicles to build a multi-layer network of global new energy vehicles and fuel vehicles (including inter-country trade relationship networks and competition relationships networks), and use this network model to analyze new energy vehicles and fuel vehicles The market competition landscape at the trade level is analyzed, and the application of multi-level exponential random graph model (ERGM) in this field is discussed. The results show that the global trade network of new energy vehicles and fuel vehicles shows an obvious center-periphery structure. Some countries have significantly maintained import relationships with multiple countries in the auto trade, and some countries have shown that they are interested in multiple countries. The propensity of a country to engage in export trade. Further ternary structure analysis revealed the existence of stratification in the trade patterns between new energy vehicles and fuel vehicles. In addition, the network structure analysis of competitive relationships shows that there are direct trade competition relationships between only a few countries, which means that only a few countries play the role of key industrial countries in the automotive trade field.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-30
DOI
https://doi.org/10.5281/zenodo.17227251
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Global Trade Competition Pattern of New Energy Vehicles and Fuel Vehicles based on Multi-Layer Network

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Analysis of Global Trade Competition Pattern of New Energy Vehicles and Fuel Vehicles based on Multi-Layer Network

article en

Abstract

Abstract— Use the 2023 global trade export data of new energy vehicles and fuel vehicles to build a multi-layer network of global new energy vehicles and fuel vehicles (including inter-country trade relationship networks and competition relationships networks), and use this network model to analyze new energy vehicles and fuel vehicles The market competition landscape at the trade level is analyzed, and the application of multi-level exponential random graph model (ERGM) in this field is discussed. The results show that the global trade network of new energy vehicles and fuel vehicles shows an obvious center-periphery structure. Some countries have significantly maintained import relationships with multiple countries in the auto trade, and some countries have shown that they are interested in multiple countries. The propensity of a country to engage in export trade. Further ternary structure analysis revealed the existence of stratification in the trade patterns between new energy vehicles and fuel vehicles. In addition, the network structure analysis of competitive relationships shows that there are direct trade competition relationships between only a few countries, which means that only a few countries play the role of key industrial countries in the automotive trade field.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 26%
Geochemistry and Geologic Mapping
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Analysis of Global Trade Competition Pattern of New Energy Vehicles and Fuel Vehicles based on Multi-Layer Network · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS