Identification and evaluation of the evolution stage of the agricultural machinery industry cluster in Shandong Province

Qiong He, Qixiao Li, Zhenlong Wan
{"title":"Identification and evaluation of the evolution stage of the agricultural machinery industry cluster in Shandong Province","authors":"Qiong He,&nbsp;Qixiao Li,&nbsp;Zhenlong Wan","doi":"10.1002/adc2.191","DOIUrl":null,"url":null,"abstract":"<p>Agricultural machinery industry clusters have great potential to solve key technological problems in China, and it is crucial to accurately identify the stage of cluster evolution. Based on the location entropy method, this paper finds that the location quotient coefficient is greater than 1.2 and the average annual growth rate is 1.11%, which indicates that the agricultural machinery industry in Shandong Province has a high degree of agglomeration, but the agglomeration speed is slow. Using the Groundings agglomeration—Economic network—Social network—Service system model, it is found that the agricultural machinery industry cluster in Shandong province is in the growth stage, in which the service system has the most significant influence on its development level. The weights of service system, social network, economic network, and basic resource aggregation derived from the Analytic Hierarchy Process model are 0.410, 0.321, 0.151, and 0.118, respectively, where agglomeration degree of the agricultural machinery industry, raw material production of agricultural machinery enterprises, exchange of tacit knowledge and intermediary service level are the four indicators with the greatest weights in the influences on sustainable development of the agricultural machinery industry. Because of the strong fuzzy nature between the indicators, this paper applies the Fuzzy Comprehensive Evaluation method to quantify the stage of evolution of Shandong Province's agricultural machinery industry cluster.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.191","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Control for Applications","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/adc2.191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Agricultural machinery industry clusters have great potential to solve key technological problems in China, and it is crucial to accurately identify the stage of cluster evolution. Based on the location entropy method, this paper finds that the location quotient coefficient is greater than 1.2 and the average annual growth rate is 1.11%, which indicates that the agricultural machinery industry in Shandong Province has a high degree of agglomeration, but the agglomeration speed is slow. Using the Groundings agglomeration—Economic network—Social network—Service system model, it is found that the agricultural machinery industry cluster in Shandong province is in the growth stage, in which the service system has the most significant influence on its development level. The weights of service system, social network, economic network, and basic resource aggregation derived from the Analytic Hierarchy Process model are 0.410, 0.321, 0.151, and 0.118, respectively, where agglomeration degree of the agricultural machinery industry, raw material production of agricultural machinery enterprises, exchange of tacit knowledge and intermediary service level are the four indicators with the greatest weights in the influences on sustainable development of the agricultural machinery industry. Because of the strong fuzzy nature between the indicators, this paper applies the Fuzzy Comprehensive Evaluation method to quantify the stage of evolution of Shandong Province's agricultural machinery industry cluster.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
山东省农机产业集群演进阶段的识别与评价
农机产业集群在解决我国关键技术问题方面潜力巨大,准确识别集群演化阶段至关重要。基于区位熵法,本文发现山东省农机产业集群的区位商系数大于 1.2,年均增长率为 1.11%,这表明山东省农机产业集聚程度较高,但集聚速度较慢。利用地缘集聚-经济网络-社会网络-服务体系模型,发现山东省农机产业集群处于成长期,其中服务体系对其发展水平的影响最为显著。由层次分析法模型得出的服务体系、社会网络、经济网络和基础资源聚集度的权重分别为 0.410、0.321、0.151 和 0.118,其中农机产业聚集度、农机企业原材料生产、隐性知识交流和中介服务水平是对农机产业可持续发展影响权重最大的四个指标。由于指标之间具有较强的模糊性,本文运用模糊综合评价法对山东省农机产业集群的演进阶段进行了量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.60
自引率
0.00%
发文量
0
期刊最新文献
Designing a Filtered Proportional–Integral–Derivative Controller With Disturbance Rejection for a Nonideal Buck Converter Utilizing an Upgraded Genetic Algorithm and Pattern Search Nonlinear Optimal Control of an H-Type Gantry Crane Driven by Dual PMLSMs Design of a Model Predictive Controlled Single-Stage Boost Assisted High Frequency Inverter for Wireless EV Charging System Control of Torque Ripple and Rotor Position for SRM (8/6-4 Phases) Using an Optimization-Based Model Predictive Torque Control Modeling of a Continuous Stirred Tank Reactor and Controller Design Using LMI Approaches
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1