摘要
选取山西省2010─2019年11个地级市的面板数据,用最邻近指数分析了山西省地理标志的时空聚集程度和演化特征;采用双向固定效应面板模型,实证探析了山西省地理标志的空间聚集对区域高质量发展的效果及作用路径。研究结果表明:在2010─2019年期间,山西省地理标志的空间聚集水平逐年上升,空间差异逐渐缩小,形成了晋城、朔州等多个高集聚区,整体呈由省界边缘向中心的阶梯式下降的聚集特征;山西省地理标志的空间聚集对于区域高质量发展有显著的正向影响,且对绿色生态、经济效率、开放创新维度的积极作用显著。基于上述研究结果,提出了以下政策建议:要进一步重视地理标志的申请、注册和保护;做大做强地理标志企业、产业;加强地理标志区域品牌建设,以充分发挥地理标志的空间聚集效应,助推区域高质量发展。
Based on the panel data of 11 prefecture-level cities in Shanxi Province from 2010 to 2019,the nearest proximity index was used to analyze spatio-temporal aggregation degree of geographical indications and evolution characteristics,and the bi-directional fixed-effect panel model was used to demonstrate the effect and path of spatial aggregation of geographical indications on regional high-quality development.The results show that the spatial aggregation level of geographical indications in Shanxi Province increases year by year,and the spatial difference narrows gradually,forming a number of high agglomeration areas such as Jincheng and Shuozhou,which show a step-down clustering feature from the provincial boundary edge to the center.Geographical indication spatial aggregation has a significant positive effect on regional high-quality development,and has a significant positive effect on green ecology,economic efficiency and open innovation.Therefore,we should pay more attention to the application,registration and protection of geographical indications,expand and strengthen enterprises and industries of geographical indications,strengthen regional brand building of geographical indications,and give full play to the agglomeration effect of geographical indications to help high-quality regional development.
作者
郭韶华
朱向梅
曹虎伟
GUO Shao-hua;ZHU Xiang-mei;CAO Hu-wei(School of Economics and Management,North University of China,Taiyuan 030051,China)
出处
《江西农业学报》
CAS
2023年第5期180-188,共9页
Acta Agriculturae Jiangxi
基金
2022年山西省政府重大决策咨询课题“乡村振兴视角下山西支持脱贫地区发展地理标志特色产业政策研究”(1206)。
关键词
山西
地理标志
空间聚集
区域高质量发展
最邻近指数
Shanxi Province
Geographical indication
Spatial aggregation
Regional high-quality development
Nearest neighbor index