中国红色旅游政策实施对网络关注度的空间溢出效应——基于语义分析与空间计量的实证

doi:10.31497/zrzyxb.20211105

摘要/Abstract

摘要：

从政策供给和公众需求的双重视角对中国红色旅游发展进行研究,有助于自上而下进行旅游资源的调配优化和自下而上调整红色旅游产品的开发创新。整合2011—2019年有关红色旅游的政务服务文本、微博信息文本和中国大陆31个省份的面板数据,使用文本语义分析比较了政策实施和网络关注的差异,从政策空间关联的视角构建空间权重矩阵,应用空间面板杜宾模型,测算红色旅游政策实施对网络关注度的空间溢出效应。结果表明：（1）自上而下的红色旅游政策实施与自下而上的网络反馈之间存在差异。（2）红色旅游网络关注度具有显著的地理和政策空间依赖性,政务服务中对传播红色文化目标的重视是网络关注度空间溢出效应产生的主要原因,政策稳定性则与红色旅游网络关注度呈负相关关系,地方政府间不稳定的竞合关系和政策发展环境的低水平稳态是导致公众对红色旅游关注度不及政策预期的可能原因。（3）在地理和政策空间关联作用下,地区公共服务能力呈现出正向溢出效应,而信息化变量表现为负向溢出效应,加强区域联动和信息化融合是红色旅游政策创新的方向。

关键词: 政策空间关联, 网络关注度, 空间溢出效应, 红色旅游

Abstract:

This paper compares the goals and implementation effects of the red tourism policy, thus helping public policy makers to allocate and optimize tourism resources. Meanwhile, it is of great significance to help market participants to adjust the development and innovation of red tourism products. Specifically, we firstly integrate government service texts and Sina Weibo texts with the theme of "red tourism" as well as relevant statistical data to form a panel dataset of 31 provincial-level regions in China from 2011 to 2019. Then, the text semantic analysis is carried out to identify policy gap. Furthermore, the spatial weight matrices are built from the perspective of policy spatial relevance. Finally, the spatial panel Durbin model is constructed to estimate the spatial spillover effect of red tourism policy on online attention from the perspective of policy spatial correlation. The findings are as follows. (1) The policy gap exists in the development of red tourism to some extent. (2) The public online attention to the red tourism is significantly dependent on geographic and policy space. The main reason for its spillover effect is that government services attach great importance to the goal of spreading the red culture, but the stability of the policy is negatively correlated with the online attention. The unstable competition and cooperation relationship among governments and the low-level steady state of the policy development environment are possible reasons that cause the public to pay less attention to red tourism than expected. (3) Under the effect of geographical and policy spatial correlation, the regional public service capacity presents a positive spillover effect, while the information variables show a negative spillover effect. Strengthening regional cooperation and integration of information service and other elements is the direction of red tourism policy innovation.

Key words: policy spatial correlation, online attention, spatial spillover effect, red tourism

戴璐, 白彩全, 梁龙武. 中国红色旅游政策实施对网络关注度的空间溢出效应——基于语义分析与空间计量的实证[J]. 自然资源学报, 2021, 36(11): 2778-2796.

DAI Lu, BAI Cai-quan, LIANG Long-wu. Spatial spillover effect of the red tourism policy on public online attention in China: An empirical study based on semantic analysis and spatial econometric model[J]. JOURNAL OF NATURAL RESOURCES, 2021, 36(11): 2778-2796.

图/表 10

图1

表1

图2

图3

图4

图5

表2

表3

表4

OLS个体固定效应模型与SDM个体固定效应模型的估计结果

模型	OLS个体固定效应		SDM个体固定效应
模型	OLS个体固定效应		复合权重		反距离权重		政策相似权重
被解释变量	lnLike	lnHot	lnLike	lnHot	lnLike	lnHot	lnLike	lnHot
解释变量	（1）	（2）	（3）	（4）	（5）	（6）	（7）	（8）
Goal1	0.95^***	0.76^**	0.83^***	0.90^***	0.93^***	0.96^***	0.82^***	0.91^***
Goal1	(2.836)	(2.256)	(3.447)	(3.241)	(3.811)	(3.442)	(3.233)	(3.126)
Goal2	-0.07	0.09	-0.11	0.03	-0.11	0.04	-0.11	0.05
Goal2	(-0.434)	(0.736)	(-1.095)	(0.255)	(-1.016)	(0.351)	(-1.034)	(0.363)
Goal3	-0.10	-0.14	-0.13	-0.22	-0.13	-0.20	-0.11	-0.19
Goal3	(-0.477)	(-0.994)	(-0.890)	(-1.340)	(-0.843)	(-1.149)	(-0.742)	(-1.137)
Goal4	-0.19	-0.34	-0.21	-0.30^*	-0.21	-0.29^*	-0.18	-0.34^**
Goal4	(-0.874)	(-1.649)	(-1.526)	(-1.911)	(-1.558)	(-1.836)	(-1.319)	(-2.150)
Polco	-0.82^*	-0.33	-0.26	-0.15	-0.22	-0.22	-0.16	-0.18
Polco	(-1.747)	(-0.718)	(-0.864)	(-0.445)	(-0.743)	(-0.635)	(-0.531)	(-0.511)
lnBiap	1.92^***	0.77^**	0.02	0.81	0.19	0.82	-0.23	0.65
lnBiap	(3.791)	(2.319)	(0.035)	(1.639)	(0.441)	(1.635)	(-0.539)	(1.359)
lnGovpay	4.44^***	-0.88	0.24	-0.55	-0.05	-0.78	-0.14	-1.17
lnGovpay	(3.945)	(-1.416)	(0.302)	(-0.610)	(-0.060)	(-0.880)	(-0.182)	(-1.390)
lnCmte	-1.30^**	-0.19	-0.71^***	-0.33	-0.75^***	-0.30	-0.71^***	-0.31
lnCmte	(-2.272)	(-0.815)	(-2.743)	(-1.108)	(-2.951)	(-1.010)	(-2.637)	(-1.013)
lnTpts	-0.36^**	0.09	-0.56	-0.78	-0.57	-0.78	-0.55	-0.80^*
lnTpts	(-2.474)	(0.592)	(-1.350)	(-1.640)	(-1.374)	(-1.642)	(-1.360)	(-1.740)
lnAccess	-2.87^***	-0.88	-1.77^***	-0.60	-1.70^***	-0.60	-1.77^***	-0.71
lnAccess	(-2.764)	(-1.113)	(-2.775)	(-0.812)	(-2.700)	(-0.827)	(-2.842)	(-0.990)
W×Goal1			-1.00	1.42	1.35	3.06	-0.65	4.56^*
W×Goal1			(-0.915)	(1.110)	(0.707)	(1.347)	(-0.273)	(1.652)
W×Goal2			-0.75	-0.51	-0.82	-0.89	-0.5	-0.06
W×Goal2			(-1.321)	(-0.777)	(-0.809)	(-0.768)	(-0.393)	(-0.041)
W×Goal3			-0.49	-1.27	-0.94	-1.58	-1.68	-2.12
W×Goal3			(-0.685)	(-1.528)	(-0.720)	(-1.048)	(-1.066)	(-1.164)
W×Goal4			0.82^*	0.53	1.27	1.47	2.04^*	0.57
W×Goal4			(1.760)	(0.995)	(1.444)	(1.478)	(1.714)	(0.424)
W×Polco			-1.01	-1.64^*	-0.73	-3.08^**	-0.04	-3.47^**
W×Polco			(-1.357)	(-1.903)	(-0.646)	(-2.253)	(-0.033)	(-2.305)
W×lnBiap			-0.70	-0.22	-1.51	1.01	-2.39^*	1.07
W×lnBiap			(-0.917)	(-0.259)	(-1.400)	(0.850)	(-1.671)	(0.758)
W×lnGovpay			6.55^***	-0.18	8.70^***	-2.79	12.33^***	-3.39
W×lnGovpay			(3.807)	(-0.112)	(3.100)	(-1.120)	(3.519)	(-1.172)
W×lnCmte			-0.45	1.18	-1.05	1.19	-0.86	3.42
W×lnCmte			(-0.357)	(0.800)	(-0.561)	(0.547)	(-0.436)	(1.504)
W×lnTpts			0.29	0.86	0.26	1.08^*	-0.12	0.75
W×lnTpts			(0.609)	(1.600)	(0.500)	(1.878)	(-0.229)	(1.430)
W×lnAccess			3.86	3.20	5.04	5.67	0.13	1.84
W×lnAccess			(1.458)	(1.092)	(1.284)	(1.302)	(0.028)	(0.382)
ρ			0.48^***	0.24^*	0.49^***	0.25^*	0.36^**	0.02
ρ			(5.195)	(1.920)	(4.263)	(1.654)	(2.457)	(0.121)
sigma²_e			0.51^***	0.67^***	0.51^***	0.67^***	0.51^***	0.66^***
sigma²_e			(11.730)	(11.790)	(11.745)	(11.795)	(11.777)	(11.811)
Observations	279	279	279	279	279	279	279	279
R²	0.802	0.111	0.325	0.047	0.349	0.112	0.285	0.095

表4

表5

SDM空间个体固定效应的分解结果

变量	效应	Goal1	Goal2	Goal3	Goal4	Polco	lnBiap	lnGovpay	lnCmte	lnTpts	lnAccess
复合权重矩阵
lnLike	直接效应	0.81^***	-0.16	-0.14	-0.17	-0.32	0.01	0.58	-0.78^***	-0.54	-1.57^**
	直接效应	(2.890)	(-1.437)	(-0.954)	(-1.272)	(-1.078)	(0.028)	(0.735)	(-2.786)	(-1.364)	(-2.261)
	间接效应	-1.13	-1.50	-1.12	1.39	-2.25	-1.22	12.58^***	-1.87	0.05	6.13
	间接效应	(-0.507)	(-1.168)	(-0.847)	(1.438)	(-1.538)	(-0.863)	(4.644)	(-0.699)	(0.090)	(1.061)
	总效应	-0.32	-1.66	-1.26	1.21	-2.57^*	-1.20	13.16^***	-2.65	-0.48	4.56
	总效应	(-0.132)	(-1.241)	(-0.900)	(1.190)	(-1.652)	(-0.810)	(4.534)	(-0.930)	(-1.174)	(0.744)
lnHot	直接效应	0.95^***	0.02	-0.23	-0.30^*	-0.19	0.85^*	-0.56	-0.33	-0.74	-0.51
	直接效应	(3.180)	(0.130)	(-1.430)	(-1.956)	(-0.554)	(1.713)	(-0.608)	(-1.146)	(-1.625)	(-0.683)
	间接效应	2.10	-0.58	-1.78^*	0.62	-2.25^**	-0.01	-0.35	1.16	0.87	4.17
	间接效应	(1.266)	(-0.629)	(-1.663)	(0.840)	(-2.013)	(-0.011)	(-0.174)	(0.611)	(1.577)	(1.017)
	总效应	3.05^*	-0.57	-2.01^*	0.32	-2.43^**	0.84	-0.91	0.83	0.13	3.66
	总效应	(1.682)	(-0.591)	(-1.771)	(0.416)	(-2.051)	(0.733)	(-0.412)	(0.411)	(0.447)	(0.841)
反距离权重矩阵
lnLike	直接效应	1.01^***	-0.15	-0.16	-0.17	-0.26	0.17	0.28	-0.84^***	-0.55	-1.48^**
	直接效应	(3.318)	(-1.124)	(-0.879)	(-1.192)	(-0.866)	(0.392)	(0.359)	(-2.937)	(-1.384)	(-2.079)
	间接效应	3.73	-1.72	-2.08	2.32	-1.85	-2.55	16.86^***	-3.55	0.03	9.31
	间接效应	(0.824)	(-0.696)	(-0.812)	(1.186)	(-0.768)	(-1.199)	(3.849)	(-0.810)	(0.047)	(0.961)
	总效应	4.74	-1.87	-2.24	2.14	-2.11	-2.37	17.15^***	-4.40	-0.52	7.83
	总效应	(1.002)	(-0.730)	(-0.833)	(1.063)	(-0.844)	(-1.068)	(3.787)	(-0.962)	(-0.835)	(0.777)
lnHot	直接效应	1.02^***	0.03	-0.21	-0.27^*	-0.26	0.87^*	-0.83	-0.30	-0.75	-0.49
	直接效应	(3.388)	(0.206)	(-1.169)	(-1.759)	(-0.786)	(1.731)	(-0.913)	(-1.073)	(-1.626)	(-0.668)
	间接效应	4.35	-1.06	-2.26	1.94	-4.29^**	1.66	-3.87	1.04	1.18^*	7.73
	间接效应	(1.448)	(-0.624)	(-1.117)	(1.272)	(-2.321)	(1.046)	(-1.171)	(0.351)	(1.921)	(1.199)
	总效应	5.37^*	-1.04	-2.47	1.67	-4.55^**	2.53	-4.70	0.73	0.44	7.24
	总效应	(1.703)	(-0.585)	(-1.163)	(1.064)	(-2.385)	(1.554)	(-1.362)	(0.239)	(1.147)	(1.083)
政策相似权重矩阵
lnLike	直接效应	0.83^***	-0.13	-0.14	-0.14	-0.16	-0.24	0.12	-0.77^***	-0.53	-1.73^**
	直接效应	(2.706)	(-1.064)	(-0.861)	(-0.985)	(-0.551)	(-0.571)	(0.166)	(-2.629)	(-1.371)	(-2.574)
	间接效应	-0.52	-0.78	-2.95	3.18	-0.39	-3.57	19.03^***	-2.55	-0.43	0.28
	间接效应	(-0.120)	(-0.336)	(-1.130)	(1.490)	(-0.187)	(-1.639)	(4.250)	(-0.629)	(-0.704)	(0.034)
	总效应	0.3	-0.91	-3.09	3.04	-0.55	-3.82^*	19.15^***	-3.32	-0.96^*	-1.45
	总效应	(0.066)	(-0.381)	(-1.141)	(1.377)	(-0.255)	(-1.686)	(4.146)	(-0.784)	(-1.884)	(-0.167)
lnHot	直接效应	0.93^***	0.04	-0.17	-0.35^**	-0.17	0.69	-1.17	-0.32	-0.78^*	-0.68
	直接效应	(3.066)	(0.324)	(-1.089)	(-2.279)	(-0.520)	(1.420)	(-1.352)	(-1.127)	(-1.734)	(-0.959)
	间接效应	4.78	0.04	-2.25	0.59	-3.63^***	1.16	-3.48	3.22	0.75	2.16
	间接效应	(1.581)	(0.027)	(-1.246)	(0.398)	(-2.620)	(0.762)	(-1.193)	(1.397)	(1.447)	(0.403)
	总效应	5.71^*	0.08	-2.43	0.25	-3.80^***	1.85	-4.65	2.90	-0.03	1.48
	总效应	(1.791)	(0.049)	(-1.299)	(0.161)	(-2.656)	(1.188)	(-1.523)	(1.205)	(-0.100)	(0.268)

表5

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变量类型	变量名称	符号	变量描述
被解释变量	用户态度	lnLike	微博用户点赞数对数值
被解释变量	关注热度	lnHot	微博用户转发和评论数之和的对数值
核心解释变量	目标重视程度	Goal	红色旅游政策目标语义重要性,Goal1~Goal4为“文化”“教育”“服务”和“资源”
核心解释变量	政策稳定性	Polco	当年与上年的红色旅游政策相似性,衡量政策发展环境
控制变量	互联网规模	lnBiap	互联网宽带接入端口对数值
	信息化基础设施	lnCmte	移动电话交换机容量对数值
	信息技术消费水平	lnTpts	邮电业务总量对数值
	公共服务能力	lnGovpay	政府一般预算支出对数值
	交通可达性	lnAccess	公路里程与建成区面积之比的对数值

年份	反距离空间权重矩阵W_ij			政策相似度权重矩阵S_ij			复合权重矩阵W^*
年份	ln(点赞)	ln(评论)	ln(转发)	ln(点赞)	ln(评论)	ln(转发)	ln(点赞)	ln(评论)	ln(转发)
2011	-0.052	0.032^**	0.026^**	-0.065	-0.014	0.010^*	-0.058	0.025^*	0.021^*
2012	0.002	0.090^***	0.028^**	-0.054	0.005^*	-0.034	-0.010	0.088^***	0.019^*
2013	0.026^**	0.058^***	0.029^**	0.004^*	0.019^**	0.009^*	0.022^*	0.051^**	0.026^*
2014	-0.021	0.010	-0.021	-0.025	-0.006	-0.021	-0.035	0.000	-0.023
2015	0.020^*	0.003	-0.037	0.019^**	-0.005	-0.030	0.017^*	0.002	-0.043
2016	0.060^***	0.050^***	0.006	0.025^**	0.024^**	0.035^***	0.065^***	0.056^***	0.015
2017	0.022^**	0.061^***	0.040^**	0.000	0.023^**	0.008^*	0.027^**	0.068^***	0.045^**
2018	0.044^**	0.049^***	0.019^*	-0.012	-0.010	-0.014	0.045^**	0.050^**	0.019^*
2019	-0.008	0.038^**	0.039^**	-0.034	-0.007	-0.016	-0.018	0.031^**	0.032^**

统计量	lnLike	lnHot	统计量	lnLike	lnHot
复合矩阵
LM_Spatial lag	197.973^***	0.211	LR_Spatial lag	29.610^***	14.420
Robust LM_Spatial lag	79.924^***	13.337^***	LR_Spatial error	47.070^***	14.320
LM_Spatial error	139.444^***	8.456^***	Wald_Spatial lag	74.160^***	25.670^***
Robust LM_Spatial error	21.395^***	21.582^***	Wald_Spatial error	58.040^***	24.950^***
Hausman	405.730^***	63.380^***
反距离矩阵
LM_Spatial lag	226.202^***	0.055	LR_Spatial lag	24.060^***	14.340
Robust LM_Spatial lag	85.434^***	15.715^***	LR_Spatial error	41.390^***	14.430
LM_Spatial error	218.323^***	6.879^***	Wald_Spatial lag	48.110^***	21.390^**
Robust LM_Spatial error	77.554^***	22.539^***	Wald_Spatial error	39.000^***	22.540^**
Hausman	126.980^***	70.380^***
政策相似矩阵
LM_Spatial lag	244.348^***	0.275	LR_Spatial lag	27.310^***	20.080
Robust LM_Spatial lag	90.719^***	13.525^***	LR_Spatial error	42.910^***	21.120^**
LM_Spatial error	247.463^***	11.526^***	Wald_Spatial lag	34.660^***	26.460^**
Robust LM_Spatial error	93.834^***	24.776^***	Wald_Spatial error	34.170^***	25.080^***
Hausman	346.780^***	84.740^***