过去30 a长三角地区经济多尺度时空特征及成因机制

Multi-scale spatiotemporal characteristics and mechanism of economy in the Yangtze River Delta in the past 30 a

关键词:  长三角  产业结构  EOF分析  时空分异特征

Key words:  Yangtze River Delta  industrial structure  EOF analysis  temporal and spatial differentiation characteristics

摘要:

长三角城市群是我国经济最为活跃的地区，也是我国经济发展的核心地区和战略支撑点。基于长三角地区27个城市的经济数据，采用EOF分析方法和空间统计分析方法，研究不同尺度下的经济时空特征及其影响因素。从空间模态上看，长三角地区省域间和市域间GDP相差悬殊，呈现从皖南地区、皖北地区、苏北地区、浙东南地区、苏南地区到上海、苏州地区的阶梯发展格局。2000、2003、2006、2009和2017年的GDP和二、三产业增加值在空间上均呈现显著的聚集效应：“高—高聚集”的城市主要分布在东部沿海地区，“低—低聚集”的城市主要分布在皖南、皖北地区，分布格局较为稳定。EOF分析的空间第二模态与经济弹性有较好的对应关系，经济弹性高的区域对应低值区（上海、苏州、无锡），而经济弹性低的区域对应高值区（合肥、南京、南通、徐州）。

Abstract:

Background, aim, and scope The Yangtze River Delta (YRD) urban agglomeration, the most economically active region in China, forms the core area and strategic support point for China’s economic development. Hence, studying the multi-temporal and spatial-scale characteristics of economic development in the YRD is of particular significance. The objective of this study is to deeply analyze the spatiotemporal characteristics of the economic data of 27 cities in the YRD region in the past 30 years and explore the causal mechanisms of these characteristics from the perspective of industrial structure. Materials and methods The GDP data of 27 cities in the YRD for the past 30 years were decomposed at different spatial and temporal scales. Methods such as empirical orthogonal function (EOF) analysis and spatial statistical analysis were used to extract the multi-temporal and spatial-scale characteristics of economic development in the YRD region, and the relationship between the changes in industrial structure and the economic changes at the unhyphenated scale was analyzed. Results (1) The spatial economic distribution of the YRD region has evolved, with Shanghai and Suzhou driving the eastern region, Xuzhou leading the north, Nanjing and Hefei contributing to central development, and Hangzhou spearheading the northwest. (2) Economic fluctuations are influenced by external shocks and industrial changes, with the second EOF mode depicting varying city responses. (3) Strategic industrial restructuring, particularly in Shanghai, has vital importance for economic resilience, thus offering a blueprint for the entire region. Discussion The GDP of the YRD region varies greatly between provinces and cities, exhibiting a ladder development pattern from southern Anhui, northern Anhui, northern Jiangsu, southeastern Zhejiang and southern Jiangsu to Shanghai and Suzhou. Further, there are significant spatial aggregation effects on the GDP and value added of secondary and tertiary industries in 2000, 2003, 2006, 2009, and 2017. Specifically, cities with “high—high clustering” are primarily distributed in the eastern coastal region, while cities with “low—low clustering”, which exhibit a relatively stable distribution pattern, are found to be concentrated in the southern and northern Anhui Province. Interannually, economic fluctuations are influenced by external shocks and changes in industrial structure, and different cities, with variations in industrial structure, respond differently to economic fluctuations. Thus, reasonable industrial restructuring is crucial to enhancing economic resilience. Conclusions This study reveals a clear pattern of economic development in the YRD region, where Shanghai and Suzhou are the leaders, forming the core of development in the east, and Nanjing, Hefei, Xuzhou, Hangzhou, and other cities each lead economic development in their respective regions. Recommendations and perspectives By underscoring the significance of adopting geographic analytical methods for economic analysis, this study recommends a focus on the refined, quantified characteristics of economic development in the YRD cities for future sustainability and growth strategies.