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Multilevel policy textual learning in Chinese local environmental policies

Wenna Chen et al.

Journal of Public Policy2025https://doi.org/10.1017/s0143814x25100652article
AJG 2ABDC B
Weight
0.44

Abstract

While existing research on policy diffusion has provided substantial evidence regarding the drivers of policy adoption across jurisdictions, limited attention has been given to the dynamics of policy textual learning across different levels of government. We fill this gap by using regression analysis to examine the patterns of policy textual learning evident in the clause similarity of seven environmental statutory policies in China. Within China’s decentralized and multilevel environmental governance, our findings reveal that horizontal policy textual learning is more prominent than vertical learning. Temporal distance negatively impacts policy textual learning, whereas spatial distance, contrary to traditional policy diffusion perspectives, does not universally explain multilevel policy textual learning. Additionally, subsequent versions of policy texts are not necessarily similar to earlier ones, challenging conventional assumptions about the adoption and adaptation of policies over time.

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https://doi.org/https://doi.org/10.1017/s0143814x25100652

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@article{wenna2025,
  title        = {{Multilevel policy textual learning in Chinese local environmental policies}},
  author       = {Wenna Chen et al.},
  journal      = {Journal of Public Policy},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1017/s0143814x25100652},
}

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Multilevel policy textual learning in Chinese local environmental policies

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.