摘要
基于熵权法建立了木材加工产业集群发展评价的指标体系,分别从集群规模、集群效益、集群市场、创新能力、集群功能5个层面来评价邳州市木材加工产业集群发展状况,并获得了邳州市2010年至2015年的集群熵的变化趋势.结果表明,产业增长率、主导产业优势和引进项目个数是影响邳州市木材加工产业集群发展的最重要因素;集群规模、集群功能推动了邳州市木材加工产业集群的发展,但集群市场和集群效益还未形成,创新能力也有待进一步提升;同时,从外部引入负熵流、抵消内部无可避免的正熵、降低系统总熵值、提高系统有序度等是实现邳州市木材加工产业集群系统可持续发展的关键.文中建立的产业集群熵度量指标体系不仅适用于木材加工产业,也为产业集群发展的理论研究提供了有益的尝试.
Based on the entropy weight method,the evaluation index system of development of wood pro- cessing industry cluster is established.By evaluating the development status of w.ood processing industry cluster in Pizhou from five aspects.cluster scale,cluster efficiency, cluster market,innovation ability and cluster function,the cluster entropy change tendency from 2010 to 2015 in Pizhou is obtained.The results show that the in^tustrial growth rate,leading industry advantages and the number of the introduced pro- jects are the most important factors influencing the development of wood processing industry cluster in Pizhou.Cluster scale, cluster function and innovation ability have promoted the development of wood pro- cessing industry cluster in Pizhou. However, the cluster market and cluster efficiency have not yet been formed.The introduction of the negative entropy flow from the outside,offsetting the unavoidable inherent positive entropy,reducing the total entropy of the systern and increasing the order of the system are the key factors to realize the sustainable development of Pizhou wood processing industry cluster system. The entropy measurement index system of industrial cluster established in this paper not only applies to the wood processing industry, but also provides a Useful attempt for the theoretical research of indus.trial cluster development.
作者
孙颖
陶诗龙
SUN Ying TAO Shi-long(College Of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China School of Sociology and Political Science,Anhui University,Hefei 230000, China)
出处
《安徽工程大学学报》
CAS
2017年第4期68-73,87,共7页
Journal of Anhui Polytechnic University
关键词
邳州市
木材加工产业
产业集群
熵权法
Pizhou City
wood processing industry
industrial cluster
entropy method