作者 | 李玲 |
姓名汉语拼音 | Li Ling |
学号 | 2021000001031 |
培养单位 | 兰州财经大学 |
电话 | 18340002939 |
电子邮件 | 1543301098@qq.com |
入学年份 | 2021-9 |
学位类别 | 学术硕士 |
培养级别 | 硕士研究生 |
学科门类 | 经济学 |
一级学科名称 | 应用经济学 |
学科方向 | 区域经济学 |
学科代码 | 020202 |
第一导师姓名 | 王娟娟 |
第一导师姓名汉语拼音 | Wang Juanjuan |
第一导师单位 | 兰州财经大学 |
第一导师职称 | 教授 |
题名 | 数字经济时代我国区域经济发展水平评价及分异研究 |
英文题名 | Research on the Effect of Digital Economy on Regional Coordinated Development |
关键词 | 数字经济 数据要素 区域经济增长 区域差异 |
外文关键词 | Digital economy; Data elements; Regional economic growth;Regional differences |
摘要 | 当前数字经济正处于高速发展阶段,成为推动产业升级、资源合理利用的关键力量。数字经济作为新时代经济发展的主要动力,利用各种信息技术推动了区域经济的发展,但同时拉大了区域间的差距,区域内部的分化也进一步加剧,出现“马太效应”。在此背景下,分析数字经济时代影响我国区域经济增长的主要
因素,各省(市、区)把握数字经济发展机遇的能力是否存在差距及差异的主要来源,对于不同地区针对性提高区域经济发展水平,推动区域协调发展具有一定的指导意义。针对上述问题,本文构建了数据要素介入前、后的两套指标体系,探究数据 要素介入前后支撑区域经济增长的指标是否发生变化;其次基于省际视角和区域 视角对数据要素介入前后各区域经济发展水平进行分析,明确各地区把握数字经 济发展机遇的能力;然后,从时间和空间两个维度探讨了数字经济时代我国区域 经济发展水平的变化趋势和空间分布格局;最后采用 Dagum 基尼系数及其分解 探究形成区域差异的原因并提出针对性的,能够有效提高区域经济发展水平的意 见及建议。研究发现:第一:从各指标权重来看,数据要素介入后支撑区域经济 增长的因素主要是数字产业化类指标;第二:从区域经济发展水平综合得分来看, 数字经济发展与传统经济发展水平呈现正相关,数据要素的介入有利于推动区域 经济增长,但各地区的经济发展基础不同,因此在把握数字经济发展机遇的能力 方面存在差异,并根据标准差(SD)和均值(M)之间的关系,将全国 30 个省 (市、区)分为领先型、进步型和落后型三种类型;第三:从数据要素介入后全 国各地区经济发展水平的空间聚集特征看,数据要素的介入虽未改变区域间的空 间正向集聚关系,但使相邻地区间的相关性减弱,数字经济时代支撑区域经济发 展的要素趋于多元化、复杂化;第四:从地区差异的贡献权重来看,区域间差异 贡献率远高于区域内差异贡献率和超变密度贡献率,表明数字经济时代,我国区 域经济发展不平衡的主要原因是区域间差异,其次是区域内差异。 |
英文摘要 | The current digital economy is in a stage of rapid development, becoming a key force in promoting industrial upgrading and rational utilization of resources. The digital economy, as the main driving force for economic development in the new era, has utilized various information technologies to promote regional economic development. However, at the same time, it has widened the gap between regions and further intensified the differentiation within regions, resulting in the "Matthew effect". In this context, analyzing the main factors that affect regional economic growth in China in the era of digital economy, and whether there is a gap in the ability of each province (city, district) to seize opportunities for digital economic development, has certain guiding significance for improving the level of regional economic development in different regions and promoting coordinated regional development. In response to the above issues, this article constructs two sets of indicator systems before and after data element intervention, exploring whether the indicators that support and hinder regional economic growth have changed before and after data element intervention; Secondly, based on the inter provincial and regional perspectives, analyze the level of economic development in each region before and after the intervention of data elements, and clarify the ability of each region to seize opportunities for digital economic development; Then, the changing trends and spatial distribution of China's regional economic development level in the digital economy era were explored from two dimensions: time and space; Finally, the Dagum Gini coefficient and its decomposition are used to explore the reasons for regional differences and propose targeted opinions and suggestions that can effectively improve the level of regional economic development. Research has found that: firstly, from the perspective of the weights of various indicators, the factors that support regional economic growth after the intervention of data elements are mainly digital industrialization indicators; Secondly, from the perspective of the comprehensive index of economic development level, the development of digital economy is positively correlated with the development level of traditional economy. The intervention of data elements is conducive to promoting regional economic growth, but the economic development foundation of each region is different. Therefore, there are differences in the ability to grasp the development opportunities of digital economy. Based on the relationship between standard deviation (SD) and mean (M), the 30 provinces (cities, districts) in China are classified as leading There are three types: progressive and backward; Thirdly, from the spatial clustering characteristics of economic development levels in various regions across the country after the intervention of data elements, although the intervention of data elements has not changed the positive spatial clustering relationship between regions, it weakens the correlation between adjacent regions, and the elements supporting regional economic development in the digital economy era tend to be diversified and complex; Fourthly, from the perspective of the contribution weight of regional differences, the contribution rate of inter regional differences is much higher than that of intra regional differences and super variable density contributions, indicating that in the digital economy era, the main reason for China's regional economic development imbalance is inter regional differences, followed by intra regional differences. |
学位类型 | 硕士 |
答辩日期 | 2024-05-25 |
学位授予地点 | 甘肃省兰州市 |
语种 | 中文 |
论文总页数 | 60 |
参考文献总数 | 68 |
馆藏号 | 0005518 |
保密级别 | 公开 |
中图分类号 | F061.5/146 |
文献类型 | 学位论文 |
条目标识符 | http://ir.lzufe.edu.cn/handle/39EH0E1M/36445 |
专题 | 经济学院 |
推荐引用方式 GB/T 7714 | 李玲. 数字经济时代我国区域经济发展水平评价及分异研究[D]. 甘肃省兰州市. 兰州财经大学,2024. |
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