From attention allocation to policy coherence: evaluating the evolution of the Yellow River Floodplain Resettlement in China

View at publisher
Authors: Long Cheng, Wenting Luo, Ben Qian Li
Cited by: 0
References: 57

Abstract

This study examines the evolution of policy and quantitatively evaluates the Yellow River Floodplain Resettlement (YRFR) in Shandong Province from 2001 to 2024. Using attention allocation theory and policy instrument theory, we compiled a database of 228 policy documents from central, provincial, and local governments. We applied advanced methods, including text mining, LDA topic modelling, and the PMC index, to conduct a comprehensive quantitative evaluation of the YRFR policy process. The results indicate that policy evolution is characterized by phased and dynamic shifts in attention allocation, with priorities transitioning from systematic resettlement to targeted poverty alleviation, livelihood security, and sustainable ecological development. Policy instrument selection shows clear hierarchical differentiation: central government policies emphasize environmental instruments, while provincial and municipal governments employ more supply-oriented tools. Although policy coherence has generally improved, problems of policy fragmentation and inconsistent allocation of policy tools remain. These findings highlight the importance of hierarchical attention and institutional incentives, such as local government promotion mechanisms, in shaping policy evolution and implementation effectiveness. This research extends attention allocation theory to multilevel governance in China, enriches existing quantitative evaluation approaches, and provides recommendations for optimizing future Yellow River Floodplain Resettlement (YRFR) policies.

Fields of Study & Subjects

China's Socioeconomic Reforms and Governance Public Policy and Administration Research Hydropower Displacement Environmental Impact

Publication details

Journal / source
Journal of Asian Public Policy
Pages
1–23
Publication date
4 February 2026
Language
EN