摘要
为明确我国各区域间生产安全状况的差异及其原因,根据2005—2019年的国内省级面板数据,分析近15年中国31个省(市自治区)的生产安全状况和产业结构水平,利用基于Driscoll Kraay标准误的双向固定效应估计模型,研究全国整体及4大经济区层面,各区域产业结构合理化和产业结构高级化对生产安全状况的影响。研究结果表明:不同经济水平和工业特征下,产业调整对生产安全状况的影响具有区域异质性,对东部、中部、西部地区起到改善作用,伴随产业合理化水平提高,东北地区事故风险上升。产业结构的优化升级有助于改善生产安全状况,通过加强区域间产业合理化和高级化的协同作用,因地制宜调整产业结构,能够促进区域生产安全和高质量经济的并行发展。
In order to clarify the differences and causes of work safety conditions among regions in China,based on the domestic provincial panel data from 2005 to 2019,this paper analyzed the work safety conditions and industrial structure levels of 31 provinces in China in the past 15 years,and used the two-way fixed effect estimation model based on Driscoll Kraay standard error to study the impact of industrial structure rationalization and industrial structure upgrading on work safety conditions at the level of the whole country and the four major economic zones.The results show that under different economic levels and industrial characteristics,the impact of industrial adjustment on work safety has regional heterogeneity,which improves the eastern,central and western regions.With the improvement of industrial rationalization level,the accident risk in Northeast China increases.The optimization and upgrading of industrial structure is conducive to improving work safety.By strengthening the synergy between regional industrial rationalization and upgrading,it can promote the parallel development of regional work safety and high-quality economy.
作者
张江石
冒香凝
郁研
李静
尤晴晴
ZHANG Jiangshi;MAO Xiangning;YU Yan;LI Jing;YOU Qingqing(School of Emergency Management and Safety Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China;School of Economy,Fudan University,Shanghai 200433,China;Institute of Urban Safety and Environmental Science,Beijing Academy of Science and Technology,Beijing 100083,China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2023年第4期29-35,共7页
China Safety Science Journal
关键词
生产安全
区域异质性
产业结构高级化
双向固定效应
亿元GDP死亡率
work safety
regional heterogeneity
advanced industrial structure
two-way fixed effect
100 million GDP mortality
