This research takes 285 prefecture-level and above cities in China's mainland as the research areas,measures the development level of digital economy by the means of panel data entropy method,analyzes its temporal and spatial evolution characteristics based on the GIS spatial analysis,and explores the driving mechanism of regional scientific and technological innovation level for the development of digital economy through panel quantile regression. The result shows that:1) From 2011 to 2018,the development level of digital economy has steadily improved in the Mainland of China,and regional differences have narrowed. This is generally reflected in the uneven spatial distribution which is higher in the east and the coast areas that in the west and the inland areas,urban agglomerations and central cities occupy a dominant position. At the same time,urban agglomerations,such as Beijing-Tianjin-Hebei,Yangtze River Delta and Pearl River Delta,and the southeastern coastal areas have also become hot spots and sub-hot spots for the development of digital economy. 2) From 2011 to 2018,the gravity center of China's digital economy generally moved to the southwest,and the overall distribution trend shows that the difference between north and south is greater than the difference between east and west. 3) According to the panel quantile regression,results show that the influence of regional scientific and technological innovation level on the development level of the digital economy shows a U-shaped change trend,and its influence first decreases and then rises with the improvement of development level of the urban digital economy,showing a strong positive overall trend enhancement. 4) For different regions,the driving effect of technological innovation on the digital economy shows a certain degree of heterogeneity,although the overall performance is a positive promotion. But there are still differences in the level of technological innovation in different regions in the driving force of different levels of digital economic development.
[1] Tapscott D.The digital economy:Promise and peril in the age of networked intelligence[M]. New York:Mc Graw-Hill,1996.
[2] Remeikiene R,Gaspareniene L,Schneider F G.The definition of digital shadow economy[J]. Technological & Economic Development of Economy,2017,24(2):696-717.
[3] 林跃勤. 新兴国家数字经济发展与合作[J].深圳大学学报:人文社会科学版,2017,34(4):105-108.
[4] Chihiro W,Kashif N,Yuji T,et al.Measuring GDP in the digital economy:Increasing dependence on uncaptured GDP[J]. Technological Forecasting and Social Change,2018,137:226-240.
[5] Fang X,Chen H C.Using vendor management inventory system for goods inventory management in IoT manufacturing[J]. Enterprise Information Systems,2021(8):1-27.
[6] Giudice M D.Discovering the Internet of Things (IoT) within the business process management[J]. Business Process Management Journal,2016,22(2):263-270.
[7] Cb A,Mqad E,Jsb C.COVID-19 pandemic digitization lessons for sustainable development of micro-and small-enterprises-ScienceDirect[J]. Sustainable Production and Consumption,2021,27(7):1989-2001.
[8] 徐智邦,王中辉,周亮,等. 中国“淘宝村”的空间分布特征及驱动因素分析[J]. 经济地理,2017,37(1):107-114.
[9] 宋周莺,刘卫东. 中国信息化发展进程及其时空格局分析[J]. 地理科学,2013,33(3):257-265.
[10] 谷国锋,许瑛航. 中国地级市电子商务发展水平的空间格局及影响因素[J]. 经济地理,2019,39(10):123-129,145.
[11] Wang H T,Hu X H,Ali N.Spatial characteristics and driving factors toward the digital economy:Evidence from prefecture-level cities in China[J]. The Journal of Asian Finance,Economics and Business,2022,9(2):419-426.
[12] Tang L Y,Lu B K,Tian T H. Spatial correlation network and regional differences for the development of digital economy in China[J]. Entropy,2021,https://doi.org/10.3390/e23121575.
[13] Li Z Q,Liu Y.Research on the spatial distribution pattern and influencing factors of digital economy development in China[J]. IEEE Access,2021(9):63094-63106.
[14] 王彬燕,田俊峰,程利莎,等. 中国数字经济空间分异及影响因素[J]. 地理科学,2018,38(6):859-868.
[15] 田俊峰,王彬燕,王士君,等. 中国东北地区数字经济发展空间分异及成因[J]. 地域研究与开发,2019,38(6):16-21.
[16] 钟业喜,毛炜圣. 长江经济带数字经济空间格局及影响因素[J]. 重庆大学学报:社会科学版,2020,26(1):19-30.
[17] 陈修颖,苗振龙. 数字经济增长动力与区域收入的空间分布规律[J]. 地理学报,2021,76(8):1882-1894.
[18] 徐维祥,周建平,刘程军. 数字经济发展对城市碳排放影响的空间效应[J]. 地理研究,2022,41(1):111-129.
[19] Sun X X,Chen Z W,Shi T T,et al.Influence of digital economy on industrial wastewater discharge:Evidence from 281 Chinese prefecture-level cities[J]. Journal of Water and Climate Change,2022,13(2):593-604.
[20] 李雪,吴福象,竺李乐. 数字经济与区域创新绩效[J]. 山西财经大学学报,2021,43(5):17-30.
[21] Sc A,Liang N B,Hsc D,et al.Digital finance,green technological innovation and energy-environmental performance:Evidence from China's regional economies[J]. Journal of Cleaner Production,2021,327:129458.
[22] 陈晓红,李杨扬,宋丽洁,等. 数字经济理论体系与研究展望[J]. 管理世界,2022,38(2):208-224,13-16.
[23] Jiang Y F.Prediction model of the impact of innovation and entrepreneurship on China's digital economy based on neural network integration systems[J]. Neural Computing and Applications,2021,34:2661-2675.
[24] 鲜祖德,王天琪. 中国数字经济核心产业规模测算与预测[J]. 统计研究,2022,39(1):4-14.
[25] 乔建永. 构建“四融合”新工程教育体系的探索[J]. 中国高等教育,2021(2):4-6.
[26] 张利国,鲍丙飞,杨胜苏. 我国农业可持续发展空间探索性分析[J]. 经济地理,2019,39(11):159-164.
[27] 方叶林,黄震方,李东和,等.中国省域旅游业发展效率测度及其时空演化[J]. 经济地理,2015,35(8):189-195.
[28] 李琼,张蓝澜,李松林,等. 中国普惠金融发展水平时空演变特征及影响因素[J]. 经济地理,2021,41(9):12-21.
[29] 刘晔,徐楦钫,马海涛. 中国城市人力资本水平与人口集聚对创新产出的影响[J]. 地理科学,2021,41(6):923-932.
[30] 赵涛,张智,梁上坤. 数字经济、创业活跃度与高质量发展——来自中国城市的经验证据[J]. 管理世界,2020,36(10):65-76.
[31] 鲁玉秀,方行明,张安全. 数字经济、空间溢出与城市经济高质量发展[J]. 经济经纬,2021,38(6):21-31.
[32] 郭峰,王靖一,王芳,等. 测度中国数字普惠金融发展:指数编制与空间特征[J]. 经济学(季刊),2020,19(4):1401-1418.
[33] 赵星,王林辉. 中国城市创新集聚空间演化特征及影响因素研究[J]. 经济学家,2020(9):75-84.
[34] 王军,朱杰,罗茜. 中国数字经济发展水平及演变测度[J]. 数量经济技术经济研究,2021,38(7):26-42.
[35] 高柏,朱兰. 从“世界工厂”到工业互联网强国:打造智能制造时代的竞争优势[J]. 改革,2020(6):30-43.
[36] 闫涛,张晓平,陈浩,等. 2001—2016年中国地级以上城市经济的区域差异演变[J]. 经济地理,2019,39(12):11-20.
[37] 彭珏,何金廖. 电商粉丝经济的地理格局及其影响因子探析——以抖音直播带货主播为例[J]. 地理科学进展,2021,40(7):1098-1112.
[38] 樊杰,王亚飞,梁博. 中国区域发展格局演变过程与调控[J]. 地理学报,2019,74(12):2437-2454.