{"title":"技术趋同与区域经济的适应能力","authors":"Luoyi Jia","doi":"10.13052/spee1048-5236.42111","DOIUrl":null,"url":null,"abstract":"This article investigates the impact of various regional technical profiles on local economies’ resistance to external shocks. It uses panel assessments of level information from 2015 to 2020 to conduct an empirical investigation of the causes of resilience. It begins by developing a calculable broad balance prototype that integrates key features of financing and the time-path of the financial system. Using a dynamic, calculable broad balance prototype recuperates with and without vibrant business adaptability. An adaptive resilience of the regional economic model (ARREM) is suggested in this paper. That integrates key features of financing and vestiges the economic growth time-path as it recuperates with and without vibrant economic adaptability. The findings show that areas with technically integrated – rather than just diverse – knowledge sets are better suited to weather adversity and exhibit adaptive resiliency. \nFurthermore, even while businesses’ net entrance doesn’t contribute considerably to durability, the domestic economy is more flexible if they develop in industries with the best development prospects. The iterative simulation results of the ARREM are examined in terms of correlation and regression. An increase in the number of iterations is made from a lower level to a higher level by 10 iterations. Because of this, the proposed ARREM’s simulation results improve with increasing iteration size.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Technical Convergence and the Adaptive Resilience of Regional Economies\",\"authors\":\"Luoyi Jia\",\"doi\":\"10.13052/spee1048-5236.42111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article investigates the impact of various regional technical profiles on local economies’ resistance to external shocks. It uses panel assessments of level information from 2015 to 2020 to conduct an empirical investigation of the causes of resilience. It begins by developing a calculable broad balance prototype that integrates key features of financing and the time-path of the financial system. Using a dynamic, calculable broad balance prototype recuperates with and without vibrant business adaptability. An adaptive resilience of the regional economic model (ARREM) is suggested in this paper. That integrates key features of financing and vestiges the economic growth time-path as it recuperates with and without vibrant economic adaptability. The findings show that areas with technically integrated – rather than just diverse – knowledge sets are better suited to weather adversity and exhibit adaptive resiliency. \\nFurthermore, even while businesses’ net entrance doesn’t contribute considerably to durability, the domestic economy is more flexible if they develop in industries with the best development prospects. The iterative simulation results of the ARREM are examined in terms of correlation and regression. An increase in the number of iterations is made from a lower level to a higher level by 10 iterations. Because of this, the proposed ARREM’s simulation results improve with increasing iteration size.\",\"PeriodicalId\":35712,\"journal\":{\"name\":\"Strategic Planning for Energy and the Environment\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Strategic Planning for Energy and the Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.13052/spee1048-5236.42111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Strategic Planning for Energy and the Environment","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.13052/spee1048-5236.42111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
Technical Convergence and the Adaptive Resilience of Regional Economies
This article investigates the impact of various regional technical profiles on local economies’ resistance to external shocks. It uses panel assessments of level information from 2015 to 2020 to conduct an empirical investigation of the causes of resilience. It begins by developing a calculable broad balance prototype that integrates key features of financing and the time-path of the financial system. Using a dynamic, calculable broad balance prototype recuperates with and without vibrant business adaptability. An adaptive resilience of the regional economic model (ARREM) is suggested in this paper. That integrates key features of financing and vestiges the economic growth time-path as it recuperates with and without vibrant economic adaptability. The findings show that areas with technically integrated – rather than just diverse – knowledge sets are better suited to weather adversity and exhibit adaptive resiliency.
Furthermore, even while businesses’ net entrance doesn’t contribute considerably to durability, the domestic economy is more flexible if they develop in industries with the best development prospects. The iterative simulation results of the ARREM are examined in terms of correlation and regression. An increase in the number of iterations is made from a lower level to a higher level by 10 iterations. Because of this, the proposed ARREM’s simulation results improve with increasing iteration size.