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Finance 2020
基于层次分析法的京津冀城市群信用环境评价
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Abstract:
作为中国三大城市群之一,京津冀城市群信用环境一直是学界重点关注的问题。本文研究的主要目的在于,研究京津冀城市群各个城市信用环境发展现状,并就如何提升信用环境提出有效的建议。依据现有的研究,本文归纳分析了信用环境的影响因素,并利用专家法及层次分析法对京津冀城市群2003~2017年信用环境进行了评价,研究发现:1) 政府信用环境是影响城市群信用环境发展的主要因素;2) 北京的信用环境水平远远高于京津冀城市群的其他几个城市,其他城市之间的信用环境水平差距相对较小;3) 随时间的推移,京津冀城市群整体的信用环境水平呈现不断提高的趋势,这与经济不断增长的趋势相吻合。
As one of the three major urban agglomerations in China, the credit environment of The Bei-jing-Tianjin-Hebei urban agglomerations has always been the focus of academic circles. The main purpose of this paper is to study the status quo of urban credit environment in The Bei-jing-Tianjin-Hebei city cluster, and put forward effective suggestions on how to improve the credit environment. Based on existing studies, this paper summarizes and analyzes the factors affecting the credit environment, and evaluates the credit environment of the Beijing-Tianjin-Hebei urban agglomeration from 2003 to 2017 by using expert analysis and analytic hierarchy process. The re-search finds that: 1) The government credit environment is the main factor affecting the develop-ment of the credit environment of the urban agglomeration; 2) The credit environment level of Bei-jing is much higher than that of several other cities in the Beijing-Tianjin-Hebei city cluster, and the gap between other cities is relatively small; 3) With the passage of time, the overall credit environ-ment level of The Beijing-Tianjin-Hebei urban agglomeration shows a trend of continuous im-provement, which is consistent with the trend of continuous economic growth.
[1] | 宋健. 基于AHP和因子分析的地区信用环境指标体系构建的实证研究[J]. 区域发展, 2006(6): 111-119. |
[2] | 姚小义, 钟心岑, 杨凯. 中国信用环境评价——基于2006~2010年的省际数据[J]. 财经理论与实践, 2013(5): 12-18. |
[3] | 郝荣, 关伟. 地区信用环境评价指标体系的探索和思考[J]. 西部金融, 2014(8): 77-80. |
[4] | Yurdakul, M. and Tanse, Y. (2004) AHP Approach in the Credit Evaluation of the Manufacturing Firms in Turkey. International Journal of Production Economics, 88, 269-289. https://doi.org/10.1016/S0925-5273(03)00189-0 |
[5] | 刘淑莲, 王真, 赵建卫. 基于因子分析的上市公司信用评级应用研究[J]. 金融与投资, 2008(7): 53-60. |
[6] | Fantazzini, D. and Gigini, S. (2009) Random Survival Forests Models for SME. Methodology and Computing in Applied Probability, 11, 29-45. https://doi.org/10.1007/s11009-008-9078-2 |
[7] | 常胜, 秦浪, 汤弟伟. 重庆市各区县综合发展水平差异分析——基于SPSS主成分分析法和聚类分析法[J]. 湖北民族学院学报(自然科学版), 2017(3): 114-122. |
[8] | 张原等. 基于因子分析的陕西省区域信用环境评价研究[J]. 北京交通大学学报, 2015, 14(2): 13-22. |
[9] | 陈泷. 城市信用评价的影响因素与对策探析[J]. 改革与开放, 2018(5): 69-70, 78. |
[10] | 王坤. 基于主成分分析的煤炭上市公司财务绩效评价[J]. 煤炭经济研究, 2019(7): 84-88. |