长三角地区间文旅产业发展差距的动态演变与影响因素研究

doi:10.3969/j.issn.1004-9479.2025.09.20240023

摘要/Abstract

摘要：

在统一大市场构建背景下，缩小地区间文旅产业的发展差距成为我国区域文旅产业实现一体化发展的关键。本文基于2010—2021年长三角41个地级市的面板数据，采用Dagum基尼系数分析了长三角地区文旅产业的发展差距及时空演化特征，并使用二次指派程序（QAP）法考察了文旅产业发展差距的影响因素。研究发现：①长三角地区文旅产业发展的整体差距显著，但该差距呈现缩小态势。②江苏省内文旅产业发展差距最大，安徽省内差距最小；比较而言，上海市与安徽省间文旅产业发展的差距最大，浙江省与江苏省间的差距最小。③省市间文旅产业发展差距相对最大，是未来推动长三角区域文旅产业一体化发展的重要着力点。④数字化水平差距和产业结构差距是影响长三角地区文旅产业发展差距的重要因素。随着时间推移，数字化水平差距的影响在变小，产业结构差距的影响在变大。不同省份文旅产业差距的主要原因不同，数字化水平差距、产业结构差距、经济发展水平差距分别是江苏省内、安徽省内、浙江省内文旅产业差距形成的主要原因。研究结论对于缩小长三角地区文旅产业发展差距，推动文旅产业一体化发展具有重要意义。

关键词: 文旅产业, 发展差距, 长三角地区, Dagum基尼系数, QAP

Abstract:

Against the backdrop of establishing a unified mega-market, how to narrow the development gap of cultural and tourism industries between regions has become the key to whether China's regional cultural and tourism industries can achieve integrated development. Drawing upon panel data spanning from 2010 to 2021 across 41 prefecture-level cities within the Yangtze River Delta, this study employs the Dagum Gini coefficient to assess the development gap and spatiotemporal evolution patterns of cultural and tourism industries across different regions within the Delta. Additionally, the Quadratic Assignment Procedure (QAP) method was used to examine the influencing factors of the development gap in the cultural and tourism industries.The study uncovers the following key findings:①A significant overall disparity in the developmental trajectories of cultural and tourism industries is evident across regions within the Yangtze River Delta, albeit demonstrating a declining trend.

毛润泽, 刘源, 刘震. 长三角地区间文旅产业发展差距的动态演变与影响因素研究[J]. 世界地理研究, 2025, 34(9): 143-157.

Runze MAO, Yuan LIU, Zhen LIU. Study on the dynamic evolution and influencing factors of the cultural and tourism industry development gap in the Yangtze River Delta region[J]. World Regional Studies, 2025, 34(9): 143-157.

图/表 9

表1 长三角地区文旅产业发展水平评价体系

Tab.1 Evaluation system for the development level of the cultural and tourism industry in the Yangtze River Delta region

目标层	系统层	指标层	属性	权重
文旅产业发展水平	产业基础	图书馆数	正	0.043
		博物馆数	正	0.072
		国家重点文物保护单位数量	正	0.062
		星级饭店数	正	0.060
		4A级及以上旅游景区数	正	0.067
		剧场/影剧院数	正	0.081
		公园数	正	0.078
	产业环境	城市(建成区)绿化覆盖率	正	0.053
		公园绿地面积	正	0.079
		城市居民人均交通通信消费支出	正	0.029
		城市居民人均教育文化娱乐服务消费支出	正	0.022
	产业效益	文化产业增加值	正	0.110
		国内旅游收入	正	0.079
		国际旅游外汇收入	正	0.165

图1 长三角41个地级市文旅产业发展水平及各维度水平的时空分异

Fig.1 Spatiotemporal differentiation of the development level and dimensional levels of the cultural and tourism industry across the 41 prefecture-level cities of the Yangtze River Delta

图2 长三角地区文旅产业整体发展差距

Fig.2 Overall development gap of the cultural and tourism industry in the Yangtze River Delta region

图3 长三角地区省份内文旅产业发展差距

Fig.3 Intra-provincial development gap in the cultural and tourism industry of the Yangtze River Delta region

图4 长三角地区三省一市间文旅产业发展差距

Fig.4 Inter-provincial development gap in the cultural and tourism industry of the Yangtze River Delta region

图5 长三角地区文旅产业发展差距来源分解

Fig.5 Decomposition of the development gap in the cultural and tourism industry of the Yangtze River Delta region

表2 QAP相关性分析结果

Tab.2 Results of QAP correlation analysis

变量	y	DIG	ECO	GOV	HUM	IND	TRA	URB
y	1.000^***
DIG	0.938^***	1.000^***
ECO	0.644^***	0.640^***	1.000^***
GOV	－0.261^**	－0.368^***	－0.745^***	1.000^***
HUM	0.313^**	0.233^*	0.375^**	－0.325^***	1.000^***
IND	0.842^***	0.794^***	0.580^***	－0.239^*	0.287^**	1.000^***
TRA	0.093	0.213^*	－0.224^*	0.281^**	－0.030	0.051	1.000^***
URB	0.707^***	0.702^***	0.869^***	－0.720^***	0.503^***	0.624^***	－0.272^**	1.000^***

表3 全样本QAP回归分析结果

Tab.3 Results of the full-sample QAP regression analysis

变量	全样本均值	2010年	2015年	2021年
DIG	0.695^***	0.638^***	0.597^***	0.473^***
ECO	0.173^**	0.087	0.306^***	0.126
GOV	0.293^***	—	0.209^***	0.060^***
HUM	0.059	0.008	0.190^***	－0.176
IND	0.143^**	0.189^**	0.236^***	0.376^***
TRA	—	0.063	—	—
URB	0.161^*	0.081	－0.089	0.238^**
调整 $R 2$	0.939^***	0.825^***	0.918^***	0.915^***
样本数	1 640	1 640	1 640	1 640

表3 全样本QAP回归分析结果

Tab.3 Results of the full-sample QAP regression analysis

变量	全样本均值	2010年	2015年	2021年
DIG	0.695^***	0.638^***	0.597^***	0.473^***
ECO	0.173^**	0.087	0.306^***	0.126
GOV	0.293^***	—	0.209^***	0.060^***
HUM	0.059	0.008	0.190^***	－0.176
IND	0.143^**	0.189^**	0.236^***	0.376^***
TRA	—	0.063	—	—
URB	0.161^*	0.081	－0.089	0.238^**
调整 $R 2$	0.939^***	0.825^***	0.918^***	0.915^***
样本数	1 640	1 640	1 640	1 640

表4 分地区QAP回归分析结果

Tab.4 Results of the sub-regional QAP regression analysis

变量	浙江省	江苏省	安徽省
DIG	0.267^**	0.662^***	—
ECO	0.423^***	－0.031	0.490^**
GOV	－0.029	－0.004	—
HUM	0.007	0.054^***	0.027
IND	0.165^*	0.126	0.752^***
TRA	0.384^**	—	—
URB	0.111^***	0.248^***	—
调整 $R 2$	0.949^***	0.973^***	0.793^***
样本数	110	156	240

表4 分地区QAP回归分析结果

Tab.4 Results of the sub-regional QAP regression analysis

变量	浙江省	江苏省	安徽省
DIG	0.267^**	0.662^***	—
ECO	0.423^***	－0.031	0.490^**
GOV	－0.029	－0.004	—
HUM	0.007	0.054^***	0.027
IND	0.165^*	0.126	0.752^***
TRA	0.384^**	—	—
URB	0.111^***	0.248^***	—
调整 $R 2$	0.949^***	0.973^***	0.793^***
样本数	110	156	240

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