Interlocal learning mechanisms and policy diffusion: The case of new energy vehicles finance in Chinese cities

Abstract Policy diffusion based on learning mechanisms has fascinated political science and public administration scholars for a long time. A robust and growing body of studies have identified the existence and importance of learning mechanisms in policy diffusion. However, there are still some gaps that need to be further improved. First, scholars identify the learning mechanism mainly based on indirect evidence, such as geographical proximity and successful innovation policies adopted by other jurisdictions, which lacking direct and systematic evidence. Second, little is known about how the hierarchical power structure affects the leap from learning behavior to policy adoption. This study provides direct evidence for the promoting effect of intergovernmental learning on policy diffusion by analyzing case of Chinese local financial subsidy policies for new energy vehicles. The empirical results reveal that policy learning in the form of site visits among local governments significantly promotes the policy diffusion, but superior government policy strategy attenuates the influence of interlocal learning on policy diffusion. Also, the initiators and themes of policy learning affect the learning–diffusion linkage, portraying the conditional effects and nuanced dynamics of interlocal policy learning in eliciting policy diffusion.

Authors

Institutions

Publication Details

Journal
Policy Studies Journal
Published
2024-11-19
DOI
https://doi.org/10.1111/psj.12576
Citations
11
Primary Topic
Policy Transfer and Learning
Type
article
Field-Weighted Citation Impact
24.54

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interlocal learning mechanisms and policy diffusion: The case of new energy vehicles finance in Chinese cities

Hongtao Yi, Liang Ma, Xuan Wang, Weixing Liu
11 citations
Policy Studies Journal
Policy Transfer and Learning
24.54
article

Interlocal learning mechanisms and policy diffusion: The case of new energy vehicles finance in Chinese cities

Hongtao Yi, Liang Ma, Xuan Wang, Weixing Liu
article en
11 citations

Abstract

Abstract Policy diffusion based on learning mechanisms has fascinated political science and public administration scholars for a long time. A robust and growing body of studies have identified the existence and importance of learning mechanisms in policy diffusion. However, there are still some gaps that need to be further improved. First, scholars identify the learning mechanism mainly based on indirect evidence, such as geographical proximity and successful innovation policies adopted by other jurisdictions, which lacking direct and systematic evidence. Second, little is known about how the hierarchical power structure affects the leap from learning behavior to policy adoption. This study provides direct evidence for the promoting effect of intergovernmental learning on policy diffusion by analyzing case of Chinese local financial subsidy policies for new energy vehicles. The empirical results reveal that policy learning in the form of site visits among local governments significantly promotes the policy diffusion, but superior government policy strategy attenuates the influence of interlocal learning on policy diffusion. Also, the initiators and themes of policy learning affect the learning–diffusion linkage, portraying the conditional effects and nuanced dynamics of interlocal policy learning in eliciting policy diffusion.

Policy Studies JournalVol. 53(1)
Florida State University (US), University of International Business and Economics (CN), Peking University (CN), Beijing Municipal Government (CN), Renmin University of China (CN)
National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities, National Social Science Fund of China
Industry, innovation and infrastructure
Openalex Percentile: Top 1%
Policy Transfer and Learning
24.54
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.