基于DPSIR-TOPSIS模型的特色小镇发展水平评价及障碍因子研究——以杭州市为例

Evaluation of the development level and study on obstacle factors of characteristic towns based on DPSIR-TOPSIS model: Taking Hangzhou as an example

为进一步成功打造特色小镇,了解特色小镇发展的关键,基于DPSIR概念模型构建特色小镇发展水平评价指标体系,采用信息熵约简法和相关性分析对指标进行优化,运用组合赋权TOPSIS方法和障碍度模型对杭州市2016—2020年特色小镇发展水平进行分析研究。研究结果表明:杭州市2016—2020年特色小镇发展水平变化波动较大,变化趋势呈现出“金字塔”型态势;从分类指标来看,除影响系统发展水平下降之外,其余系统发展水平都有不同程度的提高;从障碍度分析来看,影响杭州市特色小镇发展水平的主要因素为响应系统;从主要障碍因子来看,应从新入驻企业数、特色产业投资、基础设施建设投资增长率、固定资产投资、建城区绿化覆盖率五个指标入手对特色小镇发展进行提高。DPSIR-TOPSIS模型对杭州市小镇发展水平评价具有较好的适用性,同时该模型也为其他各地小镇发展提供理论参考,有利于加快建设国家级特色小镇的步伐。

To build better characteristic towns and understand the key to their development, we constructed an index evaluation system based on the DPSIR model. In addition to optimizing the indicators by applying the information entropy reduction method and correlation analysis, we also studied the development level of characteristic towns in Hangzhou from 2016 to 2020 using the empowered TOPSIS method and obstacle degree model. Our results showed that the development level of characteristic towns in Hangzhou fluctuated significantly from 2016 to 2020, and the trend was in a pyramid pattern. From the perspective of classification indicators, there were improvements in the development levels of all systems except for the impact system. From the analysis of obstacles, the main factor affecting the development level of Hangzhou′s characteristic towns was the response system. Regarding main obstacle factors, we should consider the number of newly settled enterprises, investment in characteristic industries, infrastructure construction investment growth rate, investment in fixed assets, urban construction, and green coverage when improving the development level of characteristic towns. The DPSIR-TOPSIS model has good applicability for evaluating the development level of small towns in Hangzhou;it provides a theoretical reference for the development of other characteristic towns and helps accelerate the pace of building national-level characteristic towns.