AUTHOR=Xinfa Tang , Yifei Sun , Chenhui Zhang , Lihong Wu , Yan Luo 

TITLE=Research on the promotion of digital teaching and learning toward achieving China’s dual-carbon strategy

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.983526

DOI=10.3389/fenvs.2023.983526

ISSN=2296-665X

ABSTRACT=In order to reduce the influence of subjective factors on the evaluation results, the Monte Carlo simulation method is used to improve the analytic hierarchy process (AHP), and the construction schedule risk assessment model of power supply and distribution engineering is built, namely, Monte Carlo simulation-Analytic Hierarchy Process (MCS-AHP). Taking a construction project in Guangdong Province as an example, a comparative study is conducted on the construction schedule evaluation model of a power supply and distribution project to measure the key factors limiting the progress of the project and to verify the feasibility of the evaluation model by combining the traditional hierarchical analysis with a comparative analysis of the evaluation index system. The analysis concludes that the arrival of material and equipment procurement and production, installation of 10kv high-voltage distribution cabinets, electrical acceptance and single unit commissioning, installation of low-voltage distribution cabinets and DC panels, and construction of power station equipment foundation are the key influencing factors, which is in line with the actual situation. Hence, the construction of the construction risk evaluation model of the power supply and distribution project is feasible and can provide a new way of thinking for schedule risk management analysis. The innovation of this paper lies in the establishment of an MCS-AHP model that uses normally distributed intervals instead of specific values in the construction of fuzzy complementary judgement matrices in order to reduce people's fuzzy thinking and the risk of incomplete information or scattered data in the process of investigation and judgement and to improve the scientific nature of evaluation.