Why Hierarchical Government Evaluations Disappear Evidence from Environmental Infrastructure and Perceived Environmental Quality

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Authors: Zhengbing Guo
Cited by: 0

Abstract

In many political systems, particularly in China, citizens exhibit a pattern of hierarchical government trust, in which confidence in central authorities tends to be higher than confidence in local administrations. Existing scholarship has largely explained this asymmetry in the context of conflictual governance settings, where local governments are directly involved in enforcement, dispute management, or community-level tensions, while the central government remains symbolically distant. Building on this literature but moving beyond conflict and enforcement contexts, this study examines whether similar evaluative dynamics emerge in the domain of urban public service provision. Specifically, it investigates whether urban infrastructure investment influences citizens’ assessments of governmental performance and whether perceived environmental quality operates as a mediating mechanism. Drawing on nationally representative survey data combined with city-level investment statistics, the analysis employs ordinary least squares regression with robust standard errors. The results show that infrastructure investment is positively associated with performance evaluations for both local and central governments, and the effect becomes stronger when the two dimensions are combined into an integrated evaluation index. Mediation tests indicate that perceived environmental quality explains part of this relationship, suggesting that improvements in everyday environmental conditions help translate investment into favorable performance assessments. These findings reveal a perceptual pathway through which material governance inputs shape public judgments and contribute to research on hierarchical government trust and performance legitimacy by showing that visible improvements in public service environments can enhance citizen evaluations across administrative levels.

Publication details

Journal / source
ICPSR Data Holdings
Publication date
1 January 2026
Language
EN
Document type
Dataset