Purpose This paper aims to investigate how the national-level construction sector policy announcements in China transmit along the upstream–midstream–downstream value chain. Focusing on strategic development, green transformation and intelligentization policies, the study examines different types of sectoral policy interventions that generate heterogeneous transmission patterns across construction-related industries. Design/methodology/approach Using daily data for 14 construction-related sub-sectors from 2010 to 2025, the study applies a higher-order co-moment framework incorporating the correlation-shift, co-skewness, co-kurtosis and co-volatility tests to capture linear, asymmetric and extreme transmission channels. A rolling contagion-hit cumulative sum (CH-CUSUM) procedure is employed to identify persistent transmission periods, and a co-movement network index (CNI) is constructed to quantify the direction, structure and segment-level distribution of policy-induced spillovers along the construction value chain. Findings Empirical results reveal distinct policy transmission patterns. Intelligentization policies generate the strongest and fastest spillovers along the construction value chain, followed by green transformation policies, while strategic development policies exhibit comparatively weaker and more diffuse transmission effects. At the segment level, the relative importance of upstream, midstream and downstream sectors varies across policy types. However, midstream construction sectors consistently exhibit higher connectivity and centrality under all three policy categories, indicating their recurrent prominence in policy transmission. Originality/value This study contributes a novel quantitative framework for tracing policy transmission across industrial value chains, combining higher-order moment analysis with dynamic network modeling. It provides new empirical evidence on the hierarchical and path-dependent nature of policy contagion in the construction sector, offering actionable insights for designing targeted policy interventions and managing sectoral risks.
Ma et al. (Tue,) studied this question.