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Legacy Knowledge, Technology Cycle Time, and Regional Catch-Up: Evidence from Automotive Innovation Clusters in China and Europe

Yan, Wenyi LU (2026) EKHS21 20261
Department of Economic History
Abstract
This thesis investigates how technology cycle time (TCT) and legacy knowledge accumulation (LKA) jointly shape EV innovation performance and regional catch-up dynamics across automotive clusters in China and Europe. Using PCT patent data (2000–2024) and HDBSCAN clustering to identify 22 Chinese and 57 European innovation clusters, the study employs PPML-HDFE and OLS panel estimators. The results show that both lower LKA and shorter TCT are associated with significantly higher EV innovation output, with stronger effects in Chinese latecomer clusters, where faster knowledge renewal supports more rapid convergence toward the European frontier. European incumbent clusters, by contrast, exhibit a gradual erosion of technological advantage under... (More)
This thesis investigates how technology cycle time (TCT) and legacy knowledge accumulation (LKA) jointly shape EV innovation performance and regional catch-up dynamics across automotive clusters in China and Europe. Using PCT patent data (2000–2024) and HDBSCAN clustering to identify 22 Chinese and 57 European innovation clusters, the study employs PPML-HDFE and OLS panel estimators. The results show that both lower LKA and shorter TCT are associated with significantly higher EV innovation output, with stronger effects in Chinese latecomer clusters, where faster knowledge renewal supports more rapid convergence toward the European frontier. European incumbent clusters, by contrast, exhibit a gradual erosion of technological advantage under conditions of high legacy dependence and slower technological iteration. These findings advance the catch-up literature by integrating knowledge renewal dynamics and legacy knowledge structure into a regional framework, providing cross-regional evidence that both dimensions shape innovation performance and catch-up trajectories during paradigm shifts. (Less)
Please use this url to cite or link to this publication:
author
Yan, Wenyi LU
supervisor
organization
course
EKHS21 20261
year
type
H1 - Master's Degree (One Year)
subject
keywords
Technological Catch-up, Path Dependence, Technology Cycle Time, Knowledge Structure, Innovation Clusters, Electric Vehicles
language
English
id
9241444
date added to LUP
2026-09-07 13:44:50
date last changed
2026-09-07 13:44:50
@misc{9241444,
  abstract     = {{This thesis investigates how technology cycle time (TCT) and legacy knowledge accumulation (LKA) jointly shape EV innovation performance and regional catch-up dynamics across automotive clusters in China and Europe. Using PCT patent data (2000–2024) and HDBSCAN clustering to identify 22 Chinese and 57 European innovation clusters, the study employs PPML-HDFE and OLS panel estimators. The results show that both lower LKA and shorter TCT are associated with significantly higher EV innovation output, with stronger effects in Chinese latecomer clusters, where faster knowledge renewal supports more rapid convergence toward the European frontier. European incumbent clusters, by contrast, exhibit a gradual erosion of technological advantage under conditions of high legacy dependence and slower technological iteration. These findings advance the catch-up literature by integrating knowledge renewal dynamics and legacy knowledge structure into a regional framework, providing cross-regional evidence that both dimensions shape innovation performance and catch-up trajectories during paradigm shifts.}},
  author       = {{Yan, Wenyi}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Legacy Knowledge, Technology Cycle Time, and Regional Catch-Up: Evidence from Automotive Innovation Clusters in China and Europe}},
  year         = {{2026}},
}