Research on quantitative evaluation of China’s Intelligent Construction Policy (CICP) based on the integration of PMC index model and multi-dimensional analytical framework

X Xiongquan Ou M Ming Ma (State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China) W Wei Wang

Abstract

This study presents a systematic evaluation of China’s Intelligent Construction Policies (CICP) from 2010 to 2022, employing a quantitative approach based on the Policy Modeling Consistency (PMC) index model. Through text mining and bibliometric analysis, we assess the formulation quality, evolution, and effectiveness of 30 national policies, identifying distinct phases of development: the Cultivation and Exploration Phase (CEP: 2010–2019) and the Development and Promotion Phase (DPP: 2020–2022). The results demonstrate a significant improvement in policy quality, with the DPP achieving higher PMC scores and more frequent issuance of “Excellent” and “Positive” policies. Key findings reveal the transition from fragmented, exploratory policies to integrated, goal-oriented strategies, emphasizing industrialization, digitalization, and sustainability. However, gaps remain in policy scope, effectiveness, and incentive mechanisms. The study concludes with targeted recommendations for optimizing CICP, including expanding policy scope, enhancing implementation frameworks, and fostering public-private innovation ecosystems to align with technological advancements and industry demands.

Article Details

Journal PLoS ONE
Volume / Issue Vol. 20, Issue 7
Published July 15, 2025
Pages e0326505
ISSN 1932-6203
Publisher Public Library of Science

Journal Info

PLoS ONE

Public Library of Science

ISSN: 1932-6203 Open Access Health Sciences

Authors (3)

X

Xiongquan Ou

M

Ming Ma

State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China

W

Wei Wang