Operations Research and Management Science ›› 2019, Vol. 28 ›› Issue (1): 125-134.DOI: 10.12005/orms.2019.0016

• Application Research • Previous Articles     Next Articles

Study on Evaluation of China’s Consumption Level Based on Fuzzy Influence Diagram

FAN De-cheng, LIHao, LIUYun   

  1. School of Economics and Management, Harbin Engineering University, Harbin 150001, China
  • Received:2014-05-04 Online:2019-01-25

基于模糊影响图的我国居民消费水平的评价研究

范德成, 李昊, 刘贇   

  1. 哈尔滨工程大学 经济管理学院,黑龙江 哈尔滨 150001
  • 作者简介:范德成(1964-),男,山东平原人,教授,博士生导师,研究方向为管理系统工程和产业结构与优化;李昊(1989-),女,黑龙江哈尔滨人,博士研究生,研究方向为产业结构与优化;刘贇(1989-),女,山东烟台人,博士研究生,研究方向为产业结构与优化。
  • 基金资助:
    国家自然科学基金资助项目(71373059);教育部人文社科项目(13YJA630016)

Abstract: In order to evaluate changes in the consumption level of residents in our country and the interaction and influence between the factors of it, this paper uses the method of fuzzy influence diagram to evaluate and analyze the index selected from the demand side, infrastructure, economic environment, policy environment and consumer itself. The results show that China’s consumption level has increased slowly with no obvious change. According to the evaluation results, combined with the topology structure of the influence diagram in terms of the factors of consumption, the openness, level of social security, consumption habits and residents’ education are the main causes of the slow consumption growth. Then the countermeasures and suggestions are put forward. These results provide theoretical reference and practical guidance for the future research on improving the China’s consumption level and the implementation about the related policy in China.

Key words: consumption economics, fuzzy influence diagram, consumption, influence factors, value node

摘要: 为了评价我国居民消费水平的变动及其影响因素之间的相互作用及影响,本文运用模糊影响图的方法,从需求方面、基础设施、经济环境、政策环境以及消费者自身五个方面选取了影响消费的十六个指标,并对这些指标进行评价分析。结果表明,我国居民消费水平增长幅度缓慢,无明显变化。根据这一评价结果,结合消费影响因素影响图的拓扑结构得出,对外开放程度、社会保障水平、消费习惯以及居民受教育程度是导致消费增长缓慢的主要原因,进而提出对策建议。为今后提高我国居民消费水平方面的研究和我国相关政策的实施提供了理论借鉴和实践指导。

关键词: 消费经济学, 消费水平, 模糊影像图, 影响因素

CLC Number: