基于空间面板计量模型的雾霾对中国城市旅游流影响的空间效应

doi:10.11821/dlxb201904014

摘要/Abstract

摘要：

以中国342个市域单元为研究对象,借助双变量LISA模型、空间面板杜宾模型等方法,探究了1998-2016年雾霾与中国城市旅游流的空间关联特征,分析了雾霾对旅游流的影响及其空间溢出效应。结果表明,在中国雾霾PM_2.5与城市旅游流有东高西低的分布特点,在胡焕庸线两侧的空间分布呈现出与地形和城市发展等因素的空间耦合性;雾霾与城市旅游流（含国内和入境旅游流）均表现出显著的空间集聚和空间依赖特征,雾霾污染对旅游流产生明显的影响并形成相应的空间效应;雾霾对旅游流的抑制区域在不断扩大,H-L型城市数量的增加、L-H型集聚区的片状扩张和华北、华中地区的L-H型集聚的“空心化”现象均表明旅游流具有低雾霾指向性;雾霾污染与旅游流的倒“U”型曲线关系表明经典的EKC假说对中国城市旅游流同样适用,且雾霾污染的显著负向影响主要存在于入境旅游方面;雾霾和旅游流均具有明显的正向空间溢出效应,将雾霾治理同经济发展、对外联系、旅游开发、生态保护和交通建设等因素结合起来进行综合治理,才能为旅游发展创造美好的环境,实现国际、国内旅游健康、协调、可持续的高质量发展。

关键词: 雾霾, PM_2.5, 旅游流, 空间关联, 空间效应, 中国

Abstract:

Taking 342 cities in China as an example, this paper explores the spatial correlation between haze (PM_2.5) and tourism flows, as well as analyzes the impact of haze on tourism flows and the spatial spillover effects from 1998 to 2016 by using bivariate LISA (Local Indications of Spatial Association) Model and Spatial Panel Dubin Model. The results show that the spatial distribution patterns of haze (PM_2.5) pollution and tourism growth in China are both high in the eastern region and low in the western region, showing some regularity related to the factors, such as terrain and urban development on both sides of Hu Huanyong population line. Haze and tourism flows (including domestic tourism flows and inbound tourism flows) show both significant spatial agglomeration and spatial dependence during the study period, indicating that haze pollution has great spatial effect on tourism flows. The area where haze curbs tourism flows is expanding. The increase in the number of HL (High-Low) -type cities, the expansion of LH (Low-High) -type agglomeration area and the hollow phenomenon of LH-type agglomeration that appear in north and central China all show that tourists tend to travel to the cities with low haze pollution. The inverted U-shape curve relationship between haze pollution and tourism flows illustrates that the classical Environmental Kuznets Curve (EKC) hypothesis is suitable for tourism growth in the cities of China. The negative impact of haze on inbound tourism flows is significant. Both haze pollution and tourism flows have positive spatial spillover effects. Combining haze management with other measures, such as economic development, tourism development, ecological protection, traffic construction, we can create a beautiful environment for tourism development and achieve a healthy, coordinated and sustainable high-quality development of international and domestic tourism.

Key words: haze, PM_2.5, tourism flow, spatial association, spatial effect, China

徐冬, 黄震方, 黄睿. 基于空间面板计量模型的雾霾对中国城市旅游流影响的空间效应[J]. 地理学报, 2019, 74(4): 814-830.

Dong XU, Zhenfang HUANG, Rui HUANG. The spatial effects of haze on tourism flows of Chinese cities: Empirical research based on the spatial panel econometric model[J]. Acta Geographica Sinica, 2019, 74(4): 814-830.

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参考文献 49

[1]	Wang Zhenbo, Fang Chuanglin, Xu Guang, et al.Spatial-temporal characteristics of the PM2.5 in China in 2014. Acta Geographica Sinica, 2015, 70(11): 1720-1734.
	[王振波, 方创琳, 许光, 等. 2014年中国城市PM2.5浓度的时空变化规律. 地理学报, 2015, 70(11): 1720-1734.]
[2]	Wang Y S, Yao L, Wang L L, et al.Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Science China Earth Sciences, 2014, 57(1): 14-25. doi: 10.1007/s11430-013-4773-4
[3]	Luan T, Guo X L, Guo L J, et al.Quantifying the relationship between PM2.5 concentration, visibility and planetary boundary layer height for long-lasting haze and fog-haze mixed events in Beijing. Atmospheric Chemistry and Physics, 2018, 18(1): 203-225. doi: 10.5194/acp-18-203-2018
[4]	Ma Limei, Zhang Xiao.The spatial of China's haze pollution and impact from economic change and energy structure. China Industrial Economics, 2014, (4): 19-31.
	[马丽梅, 张晓. 中国雾霾污染的空间效应及经济、能源结构影响. 中国工业经济, 2014, (4): 19-31.]
[5]	Yang Kun, Yang Yulian, Zhu Yanhui, et al.Social and economic drivers of PM2.5 and their spatial relationship in China. Geographical Research, 2016, 35(6): 1051-1060. doi: 10.11821/dlyj201606005
	[杨昆, 杨玉莲, 朱彦辉, 等. 中国PM2.5污染与社会经济的空间关系及成因. 地理研究, 2016, 35(6): 1051-1060.] doi: 10.11821/dlyj201606005
[6]	Huang J, Deng F R, Wu S W, et al.Comparisons of personal exposure to PM2.5 and CO by different commuting modes in Beijing, China. Science of the Total Environment, 2012, 425(3): 52-59. doi: 10.1016/j.scitotenv.2012.03.007 pmid: 22472140
[7]	Nazelle A D, Fruin S, Westerdahl D, et al.A travel mode comparison of commuters' exposures to air pollutants in Barcelona. Atmospheric Environment, 2012, 59: 151-159. doi: 10.1016/j.atmosenv.2012.05.013
[8]	Guo Wenbo, Zhang Yan, Chai Yanwei.Measurement of residents' daily travel air pollution exposure and its mechanism: A case study of suburban communities in Beijing. Geographical Research, 2015, 34(7): 1310-1318. doi: 10.11821/dlyj201507010
	[郭文伯, 张艳, 柴彦威. 城市居民出行的空气污染暴露测度及其影响机制: 北京市郊区社区的案例分析. 地理研究, 2015, 34(7): 1310-1318.] doi: 10.11821/dlyj201507010
[9]	Sönmez S F, Graefe A R.Determining future travel behavior from past travel experience and perceptions of risk and safety. Journal of Travel Research, 1998, 37(2): 171-177. doi: 10.1177/004728759803700209
[10]	Yan Youbing, Zhang Jing.Impact of smog weather on the amount of inbound tourists of China based on the natural trend curve. Economic Geography, 2016, 36(12): 183-188.
	[阎友兵, 张静. 基于本底趋势线的雾霾天气对我国入境游客量的影响分析. 经济地理, 2016, 36(12): 183-188.]
[11]	Zhang Youyin, Gu Jing, Ma Yaofeng.Tourist flow: research progress, evaluation and outlook. Tourism Tribune, 2013, 28(6): 38-46. doi: 10.3969/j.issn.1002-5006.2013.06.004
	[张佑印, 顾静, 马耀峰. 旅游流研究的进展、评价与展望. 旅游学刊, 2013, 28(6): 38-46.] doi: 10.3969/j.issn.1002-5006.2013.06.004
[12]	Yang Guoliang, Zhang Jie, Ai Nanshan, et al.Zipf structure and difference degree of tourist flow size system: A case study of Sichuan Province. Acta Geographica Sinica, 2006, 61(12): 1281-1289.
	[杨国良, 张捷, 艾南山, 等. 旅游流齐夫结构及空间差异化特征: 以四川省为例, 地理学报, 2006, 61(12): 1281-1289.]
[13]	Kulendran N, Kingb M L.Forecasting international quarterly tourist flows using error-correction and time-series models. International Journal of Forecasting, 1997, 13(3): 319-327. doi: 10.1016/S0169-2070(97)00020-4
[14]	Divino J A, McAleer M, Modelling and forecasting daily international mass tourism to Peru. Tourism Management, 2010, 31(6): 846-854. doi: 10.1016/j.tourman.2009.09.002
[15]	Zhang Jinhe, Zhang Jie, Li Na, et al.An analysis on spatial field effect of domestic tourist flows in China. Geographical Research, 2005, 24(2): 293-302. doi: 10.3321/j.issn:1000-0585.2005.02.016
	[章锦河, 张捷, 李娜, 等. 中国国内旅游流空间场效应分析. 地理研究, 2005, 24(2): 293-302.] doi: 10.3321/j.issn:1000-0585.2005.02.016
[16]	Zhang Y, Xu J H, Zhuang P J.The spatial relationship of tourist distribution in Chinese cities. Tourism Geographies, 2011, 13(1): 75-90. doi: 10.1080/14616688.2010.529931
[17]	Yang Y, Wong K K F. A spatial econometric approach to model spillover effects in tourism flows. Journal of Travel Research, 2012, 51(6): 768-778. doi: 10.1177/0047287512437855
[18]	Ji Xiaomei, Chen Jinhua, Fu Yeqin.A study of the convergence and spatial spillover effects of Chinese inbound tourism flow. Tourism Science, 2015, 29(4): 47-60. doi: 10.3969/j.issn.1006-575X.2015.04.004
	[纪小美, 陈金华, 付业勤. 中国入境旅游流的收敛与空间溢出效应分析. 旅游科学, 2015, 29(4): 47-60.] doi: 10.3969/j.issn.1006-575X.2015.04.004
[19]	Yang Xingzhu, Gu Chaolin, Wang Qun.Study on the driving force of tourist flows. Geographical Research, 2011, 30(1): 23-36. doi: 10.3724/SP.J.1011.2011.00415
	[杨兴柱, 顾朝林, 王群. 旅游流驱动力系统分析. 地理研究, 2011, 30(1): 23-36.] doi: 10.3724/SP.J.1011.2011.00415
[20]	Huang Tai.Evolution characteristics and factors affecting tourist flow potential in the Yangtze River Delta Urban Agglomerations. Resources Science, 2016, 38(2): 364-376. doi: 10.18402/resci.2016.02.18
	[黄泰. 长三角城市群旅游流潜力格局演变及其影响因素. 资源科学, 2016, 38(2): 364-376.] doi: 10.18402/resci.2016.02.18
[21]	Zhang Zi'ang, Huang Zhenfang, Cao Fangdong, et al. The space-time transition characteristics and its driving mechanism of county-scale inbound tourism in Zhejiang province. Geographical Research, 2016, 35(6): 1177-1192. doi: 10.11821/dlyj201606014
	[张子昂, 黄震方, 曹芳东, 等. 浙江省县域入境旅游时空跃迁特征及驱动机制. 地理研究, 2016, 35(6): 1177-1192.] doi: 10.11821/dlyj201606014
[22]	Sun Gennian, Zhou Ruina, Ma Lijun, et al.Impact of five important events on China provincial inbound tourism in 2008: An analysis in high-resolution based on the TBL model. Scientia Geographica Sinica, 2011, 31(12): 1437-1446.
	[孙跟年, 周瑞娜, 马丽君, 等. 2008年五大事件对中国入境旅游的影响: 基于本底趋势线模型高分辨率的分析. 地理科学, 2011, 31(12): 1437-1446.]
[23]	Cheng Li, Zhang Tonghao, Fu Yang.Urban residents' cognition of haze-fog weather and its impact on their urban tourism destination choice. Tourism Tribune, 2015, 30(10): 37-47. doi: 10.3969/j.issn.1002-5006.2015.10.004
	[程励, 张同颢, 付阳. 城市居民雾霾天气认知及其对城市旅游目的地选择倾向的影响. 旅游学刊, 2015, 30(10): 37-47.] doi: 10.3969/j.issn.1002-5006.2015.10.004
[24]	Li Jing, Philip L P, Wu Bihu, et al.The impact of smog on risk perception and satisfaction of international and domestic tourists in Beijing. Tourism Tribune, 2015, 30(10): 48-59. doi: 10.3969/j.issn.1002-5006.2015.10.005
	[李静, Philip L P, 吴必虎, 等. 雾霾对来京旅游者风险感知及旅游体验的影响: 基于结构方程模型的中外旅游者对比研究. 旅游学刊, 2015, 30(10): 48-59.] doi: 10.3969/j.issn.1002-5006.2015.10.005
[25]	Zhang Chen, Gao Jun, Ding Peiyi.Impacts of haze on the intention to visit China of overseas tourists: Based on destination image and risk perception theories. Tourism Tribune, 2017, 32(12): 58-67. doi: 10.3969/j.issn.1002-5006.2017.12.013
	[张晨, 高俊, 丁培毅. 雾霾天气对潜在海外游客来华意愿的影响: 基于目的地形象和风险感知理论. 旅游学刊, 2017, 32(12): 58-67.] doi: 10.3969/j.issn.1002-5006.2017.12.013
[26]	Peng Jian, Guo Siyuan, Pei Yanan, et al.Mainland Chinese residents' perception of tourism impacted by haze in Beijing and their attitudes. China Population, Resources and Environment, 2016, 26(10): 168-176. doi: 10.3969/j.issn.1002-2104.2016.10.022
	[彭建, 郭思远, 裴亚楠, 等. 大陆居民对北京雾霾的旅游影响感知和态度研究. 中国人口资源与环境, 2016, 26(10): 168-176.] doi: 10.3969/j.issn.1002-2104.2016.10.022
[27]	Tang Chengcai, Liu Xiaoquan, Song Changyao.Impact of haze on regional tourism industry and its countermeasures. Geography and Geo-Information Science, 2016, 32(5): 121-126.
	[唐承财, 刘霄泉, 宋昌耀. 雾霾对区域旅游业的影响及应对策略探讨. 地理与地理信息科学, 2016, 32(5): 121-126.]
[28]	Han Ye, Zhou Zhongxue.Evaluation on ecosystem services in haze absorption by urban green land and its spatial pattern analysis in Xi'an. Geographical Research, 2015, 34(7): 1247-1258. doi: 10.11821/dlyj201507005
	[韩晔, 周忠学. 西安市绿地景观吸收雾霾生态系统服务测算及空间格局. 地理研究, 2015, 34(7): 1247-1258.] doi: 10.11821/dlyj201507005
[29]	Lippmann M.Toxicological and epidemiological studies of cardiovascular effects of ambient air fine particulate matter (PM2.5) and its chemical components: Coherence and public health implications. Critical Reviews in Toxicology, 2014, 44(4): 299-347. doi: 10.3109/10408444.2013.861796
[30]	Pope C A, Brook R D, Burnett R T, et al.How is cardiovascular disease mortality risk affected by duration and intensity of fine particulate matter exposure? An integration of the epidemiologic evidence. Air Quality, Atmosphere and Health, 2011, 4(1): 5-14. doi: 10.1007/s11869-010-0082-7
[31]	Eitan O, Yuval, Barchana M, et al.Spatial Analysis of air pollution and cancer incidence rates in Haifa Bay, Israel. Science of the Total Environment, 2010, 408(20): 4429-4439. doi: 10.1016/j.scitotenv.2010.06.031 pmid: 20630566
[32]	Huang K, Zhuang G, Lin Y, et al.Typical types and formation mechanisms of haze in an Eastern Asia megacity, Shanghai. Atmospheric Chemistry and Physics, 2012, 12(1): 105-124. doi: 10.5194/acp-12-105-2012
[33]	Shao Shuai, Li Xin, Cao Jianhua, et al.China's economic policy choices for governing smog pollution based on spatial spillover effects. Economic Research Journal, 2016, 51(9): 73-88.
	[邵帅, 李欣, 曹建华, 等. 中国雾霾污染治理的经济政策选择: 基于空间溢出效应的视角. 经济研究, 2016, 51(9): 73-88.]
[34]	Anselin L, Syabri I, Smirnov O.Visualizing multivariate spatial correlation with dynamically linked windows//Anselin L, REY S. New Tools for Spatial Data Analysis: Proceedings Of The Specialist Meeting. Center for Spatially Integrated Social Science, University of California, Santa Barbara, CD-ROM, 2002.
[35]	Elhorst J P.Specification and estimation of spatial panel data models. International Regional Science Review, 2003, 26(3): 244-268. doi: 10.1177/0160017603253791
[36]	Lesage J, Pace R K.Introduction to Spatial Econometrics. New York, CRC Press, 2009.
[37]	Wang kun, Huang Zhenfang, Yu Fenglong, et al. Spatial effects of China's urbanization on tourism economic development: Empirical research based on the spatial panel econometric model. Tourism Tribune, 2016, 31(5): 15-25. doi: 10.3969/j.issn.1002-5006.2016.05.007
	[王坤, 黄震方, 余凤龙, 等. 中国城镇化对旅游经济影响的空间效应: 基于空间面板计量模型的研究. 旅游学刊, 2016, 31(5): 15-25.] doi: 10.3969/j.issn.1002-5006.2016.05.007
[38]	Van D A, Martin R V, Brauer M, et al.Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environmental Health Perspectives, 2015, 123(2): 135-143. doi: 10.1289/ehp.1408646 pmid: 4314252
[39]	Dai Zhaoxin, Zhang Yunzhi, Hu Yunfeng, et al.Spatial-temporal characteristics of PM2.5 in Yangtze River Delta (YRD) region based on the ground monitoring data from 2013-3015. Resources and Environment in the Yangtze Basin, 2016, 25(5): 813-821. doi: 10.11870/cjlyzyyhj201605015
	[戴昭鑫, 张云芝, 胡云锋, 等. 基于地面监测数据的2013-2015年长三角地区PM2.5时空特征. 长江流域资源与环境, 2016, 25(5): 813-821.] doi: 10.11870/cjlyzyyhj201605015
[40]	Lee S J, Serre M L, Van D A, et al.Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States. Environmental Health Perspectives, 2012, 120(12): 1727-1732.] doi: 10.1289/ehp.1205006
[41]	Pan Jinghu, Zhang Wen, Li Junfeng, et al.Spatial distributional characteristics of air pollutants in major cities in China during the period of wide range haze pollution. Chinese Journal of Ecology, 2014, 33(12): 3423-3431.
	[潘竟虎, 张文, 李俊峰, 等. 中国大范围雾霾期间主要城市空气污染物分布特征. 生态学杂志, 2014, 33(12): 3423-3431.]
[42]	Liu Junsheng, Ma Yaofeng.The evolution and driving mechanism of the supply and demand coupling about tourism flow and destination system based on the genesis and system theory: Taking Xi'an as an example. Geographical Research, 2017, 36(8): 1583-1600. doi: 10.11821/dlyj201708015
	[刘军胜, 马耀峰. 基于发生学与系统论的旅游流与目的地供需耦合成长演化与驱动机制研究: 以西安市为例. 地理研究, 2017, 36(8): 1583-1600.] doi: 10.11821/dlyj201708015
[43]	Wang Degen, Chen Tian, Lu Lin, et al.Mechanism and HSR effect of spatial structure of regional tourist flow: Case study of Beijing-Shanghai HSR in China. Acta Geographica Sinica, 2015, 70(2): 214-233. doi: 10.11821/dlxb201502004
	[汪德根, 陈田, 陆林, 等. 区域旅游流空间结构的高铁效应及机理: 以中国京沪高铁为例. 地理学报, 2015, 70(2): 214-233.] doi: 10.11821/dlxb201502004
[44]	Liu Y P, Li Y C, Parkpian P.Inbound tourism in Thailand: Market form and scale differentiation in ASEAN source countries. Tourism Management, 2018, 64(1): 22-36. doi: 10.1016/j.tourman.2017.07.016
[45]	Wang Kun, Huang Zhenfang, Cao Fangdong, et al.Spatial pattern evolution of urban tourism performance and its influence factors in Pan-Yangtze River Delta. Journal of Natural Resources, 2016, 31(7): 1149-1163.
	[王坤, 黄震方, 曹芳东, 等. 泛长江三角洲城市旅游绩效空间格局演变及其影响因素. 自然资源学报, 2016, 31(7): 1149-1163.]
[46]	Zhou Liang, Zhou Chenghu, Yang Fan, et al.Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2011. Acta Geographica Sinica, 2017, 72(11): 2709-2092. doi: 10.11821/dlxb201711012
	[周亮, 周成虎, 杨帆, 等. 2000-2011年中国PM2.5时空演化特征及驱动因素解析. 地理学报, 2017, 72(11): 2079-2092.] doi: 10.11821/dlxb201711012
[47]	Merbitz H, Buttstädt M, Michael S, et al.GIS-based identification of spatial variables enhancing heat and poor air quality in urban areas. Applied Geography, 2012, 33(1): 94-106. doi: 10.1016/j.apgeog.2011.06.008
[48]	Liu Haimeng, Fang Chuanglin, Huang Xiejun, et al.The spatial-temporal characteristics and influencing factors of air pollution in Beijing-Tianjin-Hebei urban agglomeration. Acta Geographica Sinica, 2018, 73(1): 177-191. doi: 10.11821/dlxb201801015
	[刘海猛, 方创琳, 黄解军, 等. 京津冀城市群大气污染的时空特征与影响因素解析. 地理学报, 2018, 73(1): 177-191.] doi: 10.11821/dlxb201801015
[49]	Huang Zhenfang, Huang Rui.The theoretical perspective and academic innovation of tourism geography based on human-environment interactions. Geographical Research, 2015, 34(1): 15-26. doi: 10.11821/dlyj201501002
	[黄震方, 黄睿. 基于人地关系的旅游地理学理论透视与学术创新. 地理研究, 2015, 34(1): 15-26.] doi: 10.11821/dlyj201501002

年份	单变量Moran′s I				双变量Moran′s I
年份	Pm	TF	DTF	ITF	Pm与TF	Pm与DTF	Pm与ITF
1998-2000	0.7411^***	0.1539^***	0.1570^***	0.1708^***	0.2100^***	0.2266^***	-0.1103^***
1999-2001	0.8192^***	0.1507^***	0.1541^***	0.2623^***	0.2096^***	0.2285^***	-0.1536^***
2000-2002	0.8408^***	0.1474^***	0.1512^***	0.2654^***	0.2299^***	0.2468^***	-0.1482^***
2001-2003	0.8373^***	0.1533^***	0.1551^***	0.2771^***	0.2673^***	0.2805^***	-0.1242^***
2002-2004	0.8149^***	0.1592^***	0.1614^***	0.2118^***	0.3002^***	0.3096^**	-0.0661^**
2003-2005	0.8082^***	0.1641^***	0.1702^***	0.2200^***	0.3241^***	0.3354^***	-0.0264
2004-2006	0.7910^***	0.1715^***	0.1828^***	0.2163^***	0.3216^***	0.3392^***	-0.0045
2005-2007	0.8211^***	0.1746^***	0.1820^***	0.2120^***	0.3236^***	0.3353^***	0.0248
2006-2008	0.8267^***	0.1769^***	0.1838^***	0.2220^***	0.3273^***	0.3372^***	0.0603^***
2007-2009	0.8333^***	0.1740^***	0.1790^***	0.2324^***	0.3425^***	0.3369^***	0.1106^***
2008-2010	0.8106^***	0.1775^***	0.1826^***	0.2699^***	0.3356^***	0.3316^***	0.2591^***
2009-2011	0.8036^***	0.1805^***	0.1871^***	0.2800^***	0.3333^***	0.3281^***	0.1035^***
2010-2012	0.8001^***	0.1852^***	0.1921^***	0.2777^***	0.3254^***	0.3269^***	0.1031^***
2011-2013	0.8242^***	0.1856^***	0.1931^***	0.2737^***	0.3108^***	0.3123^***	0.0807^***
2012-2014	0.8284^***	0.1837^***	0.1895^***	0.2660^***	0.2655^***	0.2684^***	0.0637^**
2013-2015	0.8656^***	0.1789^***	0.1856^***	0.2487^***	0.2334^***	0.2384^***	0.0038
2014-2016	0.8427^***	0.1769^***	0.1840^***	0.2400^***	0.1845^***	0.1909^***	-0.0254

检验方法	统计值	概率	检验方法	统计量
LM-spatial lag	241.6970	0.0000	Wald-spatial lag	266.6742
Robust LM-spatial lag	16.9235	0.0000	LR-spatial lag	278.9365
LM-spatial error	298.9592	0.0000	Wald-spatial error	421.0918
Robust LM-spatial error	74.1858	0.1220	LR-spatial error	410.1196

变量	个体固定效应	空间面板杜宾模型		变量	空间面板杜宾模型
变量	个体固定效应	无固定效应	空间固定效应	变量	无固定效应		空间固定效应
lnPm	0.1882^*** (7.8634)	0.1656^*** (7.46)	0.1823^*** (7.80)	W×lnPm		0.0134^* (0.20)	-0.0313^* (-0.45)
lnPgdp	0.9976^*** (56.0175)	0.8694^*** (44.11)	0.9300^*** (45.93)	W×lnPgdp		-0.1968^*** (-4.53)	-0.2665^*** (-5.28)
lnDen	0.0398 (0.7848)	0.1169^*** (3.73)	-0.0345 (-0.70)	W×lnDen		0.2435^*** (4.89)	0.6403^*** (3.70)
lnFDI	0.0264^*** (3.4618)	0.0262^*** (3.44)	0.0144^* (1.86)	W×lnFDI		0.0061 (0.34)	0.0312^* (1.66)
lnRes	0.0426^*** (10.5688)	0.0482^*** (11.87)	0.0436^*** (10.63)	W×lnRes		-0.0227 (-2.36)	-0.0205^** (-1.99)
lnTra	0.0931^*** (4.8390)	0.1213^*** (6.10)	0.1065^*** (5.25)	W×lnTra		-0.2432^*** (-5.55)	-0.2347^*** (-5.21)
lnInf	0.0107^*** (0.8622)	0.0173 (1.30)	-0.0175 (-1.31)	W×lnInf		0.0760^*** (2.75)	0.1113^*** (3.94)
Adj.R²	0.9332	0.6192	0.9507	ρ		0.3810^***	0.3766^***
Log L	-2563.2010	-3317.8073	-2406.0722	ρ		(16.27)	(14.56)

变量	个体固定效应		SPDM空间固定效应		变量	SPDM空间固定效应
变量	DTF	ITF	DTF	ITF	变量	DTF	ITF
lnPm	0.1937^*** (8.0014)	-0.0747^* (-1.4314)	0.1825^*** (7.75)	-0.0734^* (8.0014)	W×lnPm	-0.0557^* (-0.79)	0.1057^* (0.71)
lnPgdp	1.0083^*** (55.9759)	0.6369^*** (16.3964)	0.9640^*** (47.22)	0.5704^*** (13.29)	W×lnPgdp	-0.3558^*** (-7.07)	-0.3417^*** (-3.74)
lnDen	0.1277^** (2.4907)	-0.3867^*** (-3.4963)	0.0445 (0.89)	-0.4047^** (-3.88)	W×lnDen	0.6350^** (3.63)	0.2612 (0.71)
lnFDI	0.0282^*** (3.6613)	0.1742^*** (10.4835)	0.0199^** (2.54)	0.1319^*** (8.01)	W×lnFDI	0.0169 (0.89)	0.0651 (1.63)
lnRes	0.0440^*** (10.8009)	0.0078 (0.8910)	0.0424^*** (10.24)	0.0164^* (1.89)	W×lnRes	-0.0119 (-1.15)	-0.0240 (-1.11)
lnTra	0.0661^*** (3.3934)	0.1470^*** (3.5012)	0.0765^*** (3.74)	0.1096^** (2.55)	W×lnTra	-0.2160^*** (-4.76)	-0.1040 (-1.09)
lnInf	0.0336^*** (2.6761)	0.0098^*** (0.7175)	-0.0070 (-0.52)	0.0104 (0.37)	W×lnInf	0.1361^*** (4.78)	0.0089 (0.15)
Adj.R²	0.9328	0.8697	0.9649	0.8890	ρ	0.4109^***	0.5387^***
Log L	-2629.9890	-7097.2490	-2460.2492	-6563.8788	ρ	(16.13)	(24.29)

效应		lnPm	lnPgdp	lnDen	lnFDI	lnRes	lnTra	lnInf
直接效应	TF	0.1840^*** (7.80)	0.9338^*** (47.22)	-0.0060 (-0.12)	0.0161^** (2.10)	0.0434^*** (10.68)	0.0977^*** (4.88)	-0.0128 (-0.97)
	DTF	0.1834^*** (7.68)	0.9660^*** (48.49)	0.0774 (1.53)	0.0212^** (2.73)	0.0427^*** (10.38)	0.0672^*** (3.33)	-0.0003 (-0.02)
	ITF	-0.0687^* (-1.34)	0.5679^*** (13.55)	-0.4016^*** (-3.69)	0.1418^*** (8.63)	0.0153^* (1.76)	0.1063^** (2.49)	0.0114 (0.41)
溢出效应	TF	0.0309 (0.54)	0.0694^** (2.13)	0.5197^*** (3.65)	0.0303^** (2.03)	-0.0033 (-0.41)	-0.1612^*** (-4.50)	0.0867^*** (3.92)
	DTF	0.0166 (0.28)	0.0348 (1.02)	0.5641^*** (3.77)	0.0217 (1.38)	0.0047 (0.55)	-0.1594^*** (-4.25)	0.1151^*** (4.95)
	ITF	0.0684^* (0.45)	-0.0356 (-0.42)	0.0447 (0.12)	0.1409^*** (3.58)	-0.0156 (-0.73)	-0.0465 (-0.50)	0.0150 (0.26)
总效应	TF	0.2149^*** (3.30)	1.0032^** (29.47)	0.5137^*** (3.26)	0.0464^*** (2.84)	0.0401^*** (4.43)	-0.0635 (-1.60)	0.0739^*** (3.10)
	DTF	0.2001^*** (2.92)	1.0009^*** (27.94)	0.6415^*** (3.86)	0.0428^** (2.49)	0.0474^*** (4.98)	-0.0922^** (-2.21)	0.1148^*** (4.57)
	ITF	-0.0002^* (-0.00)	0.5323^*** (5.89)	-0.3569 (-0.85)	0.2826^*** (6.48)	-0.0003 (-0.01)	0.0598 (0.57)	0.0264 (0.42)