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地理研究    2018, Vol. 37 Issue (3): 539-550     DOI: 10.11821/dlyj201803007
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
居民幸福感的城际差异及其影响因素探析——基于多尺度模型的研究
党云晓1(),张文忠2,3(),谌丽4,湛东升2,3
1. 浙江财经大学土地与城乡发展研究院,杭州 310018
2. 中国科学院地理科学与资源研究所,区域可持续发展分析与模拟重点实验室,北京 100101
3. 中国科学院大学,北京 1000493
4. 北京联合大学应用文理学院,北京 100191
Inter-city difference and influencing factors of residents' subjective well-being: A study based on multilevel modelling
DANG Yunxiao1(),ZHANG Wenzhong2,3(),CHEN Li4,ZHAN Dongsheng2,3
1. College of Land and Urban-rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, China
2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
3. China University of Chinese Academy of Sciences, Beijing 100039, China
4. College of Applied Arts and Science, Beijing Union University, Beijing 100191, China
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摘要 

居民幸福感是建设和谐社会与检验城镇化质量的重要内容。地理学视角的研究围绕幸福感空间差异展开,解读背景环境特征与其关系。国内已有研究缺乏基于个体与城市尺度的联合分析,且缺少城市特征对幸福感影响机制的综合考虑。以环渤海地区44个城市为例,基于大规模调研问卷,采用多尺度建模方法,分析居民幸福感的城际差异及影响因素。结果表明,居民幸福感在城市之间存在显著差异;经济最发达的城市居民幸福感最低;城市规模对居民幸福感有负面影响,收入可以缓解城市规模负面影响的程度;环境污染对居民幸福感有显著的负面影响,积极的环境评价对居民幸福感有正面影响;良好的社会治安与人文环境对居民幸福感影响为正。

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党云晓
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谌丽
湛东升
关键词 幸福感城际差异多尺度模型环渤海地区 
Abstract

During the last three decades, China has been marked by a remarkable economic growth, however, the greatly enriched material life did not promise Chinese an equal level of happiness. It has been proved that Chinese subjective well-being has been decreasing during the progress of high-speed urbanization. Scholars in Western world have conducted abundant research in influencing factors of residents' subjective well-being and evaluation of happy city. There is lack of studies aimed at Chinese residents, especially the systematic analysis of the influence by geographical background effect posed on the residents' subjective well-being. Taking all the prefecture-level cities in the Bohai Rim area as a case study, based on large sample survey questionnaires, objective statistics and census data, this paper analyzed the spatial distribution of subjective well-being at inter-city scale. Happiness function improved by multilevel modeling and GIS-based spatial analysis method are also applied to analyze the influencing factors of residents' subjective well-being. The conclusions are as follows: (1) There is significant disparity of residents' subjective well-being between cities. Most of the cities with more happy samples are in Liaoning province, while those with more unhappy samples are found in the majority of cities in Hebei province except Zhangjiakou and Chengde. (2) Urban scale and economic development are negatively related to residents' subjective well-being. People living in the biggest city are most unhappy with life, however, the high income can weaken the unhappiness. (3) Environment pollution has reduced the residents' subjective well-being, while positive evaluation of natural environment is helpful to improve residents' happiness. (4) Social security and human environment are positively related to the subjective well-being. The widening wealth inequality and urban diseases, such as pollution, traffic jam and housing shortage, should be responsible for the lowest degree of subjective well-being of residents living in the biggest cities. Even so, people are more likely to live in big cities. One possible reason is that the individual may be willing to trade off the subjective well-being for other things including high income, social status and accomplishments. Furthermore, individuals may prefer to take actions enabling to achieve the long-term desires and goals, although some of them could make them less happier at present.

Key wordssubjective well-being    spatial difference    multilevel modelling    Bohai rim area
收稿日期: 2017-10-26      出版日期: 2018-04-25
基金资助:国家自然科学基金项目(41701184,41601160,41230632)
引用本文:   
党云晓, 张文忠, 谌丽等 . 居民幸福感的城际差异及其影响因素探析——基于多尺度模型的研究[J]. 地理研究, 2018, 37(3): 539-550.
DANG Yunxiao, ZHANG Wenzhong, CHEN Li et al . Inter-city difference and influencing factors of residents' subjective well-being: A study based on multilevel modelling[J]. GEOGRAPHICAL RESEARCH, 2018, 37(3): 539-550.
链接本文:  
http://www.dlyj.ac.cn/CN/10.11821/dlyj201803007      或      http://www.dlyj.ac.cn/CN/Y2018/V37/I3/539
分类 指标 均值/标准差 数据来源
城市层级变量
规模与经济指标 GDP(亿元) 1958.61/3479.19 城市统计年鉴2014
年末总人口(万人) 187.82/217.81 城市统计年鉴2014
人口密度(人/km2 1375.05/1299.84 城市统计年鉴2014
在岗职工平均工资(元) 48364.74/10616.30 城市统计年鉴2014
环境指标 灰霾暴露天数(天) 147.41/69.71 北京城市实验室
工业废水排放量(万t) 9341.40/6659.53 城市统计年鉴2014
环境舒适度评价 65.39/9.43 问卷调研
环境健康性评价 57.23/12.56 问卷调研
社会人文指标 社会治安评价 66.31/6.11 问卷调研
失业率 4.52/2.81 城市统计年鉴2014
人文环境舒适度评价 66.5/5.98 问卷调研
个体层级变量
生活幸福感评价 非常幸福(17.17%);幸福(51.51%);一般(26.68%);不幸福(2.83%);很不幸福(0.88%)
年龄 <20岁(3.58%);20~29岁(39.79%);30~39岁(29.2%);40~49岁(19.79%);50~59岁(5.49%);>60岁(1.89%)
性别 男(52.41%);女(46.76%)
家庭月总收入(元) <3000(16.34%);3000~5000(30.33%);5000~10000(34.02%);10000~15000(11.19%);15000~20000(3.52%);20000~30000(1.21%);>30000(1.45%)
户籍 本地户籍(82.92%);外地户籍(14.28%)
婚姻 已婚(66.58%);单身(31.78%)
就业状况 全职(80.92%);兼职(9.05%);家庭主妇(2.6%);退休(2.54%);待业(2.74%)
Tab.1  环渤海地区城市特征及问卷调研样本属性统计
Fig. 1  环渤海地区城市居民幸福水平空间差异
变量 模型I 模型II 模型III 模型IV
城市层级
GDP(亿元)
<500 0.235**(0.135)
500~1000 0.263**(0.126)
1000~2000 0.424***(0.157)
总人口(万)
100~200 -0.17*(0.107)
200~500 -0.255**(0.141)
>500 -0.373**(0.2)
人口密度 -0.243(0.382)
平均工资 -0.902***(0.293)
个体层级
男性 -0.07**(0.032) -0.069**(0.033) -0.07**(0.034) -0.069**(0.033)
年龄 -0.02(0.021) -0.018(0.021) -0.017(0.022) -0.021(0.02)
家庭收入 0.146***(0.02) 0.147***(0.019) 0.143***(0.02) 0.145***(0.019)
外地人 -0.259***(0.048) -0.253***(0.049) -0.261***(0.048) -0.257***(0.048)
单身 -0.314***(0.044) -0.306***(0.043) -0.308***(0.044) -0.314***(0.043)
就业状况
兼职 0.026(0.061) 0.026(0.063) 0.024(0.061) 0.031(0.06)
家庭主妇 0.213**(0.116) 0.216**(0.112) 0.214**(0.117) 0.21**(0.12)
退休 0.222**(0.121) 0.224**(0.121) 0.216**(0.123) 0.223**(0.118)
待业 -0.062(0.101) -0.066(0.101) -0.072(0.101) -0.073(0.101)
常量 0.45***(0.116) 0.776***(0.099) 0.691***(0.107) 1.115***(0.148)
城市层级方差 0.093 0.095 0.106 0.1
DIC 7881.01 7879.29 7879.88 7879.88
pD 48.628 47.969 48.309 47.964
Tab.2  城市规模与经济指标的多尺度模型回归结果
变量 模型V 模型VI 模型VII 模型VIII
城市层级
灰霾暴露 -0.155**(0.074)
工业废水 -0.053(0.085)
环境舒适度 1.338***(0.295)
环境健康性 1.275***(0.346)
个体层级
男性 -0.065**(0.034) -0.068**(0.034) -0.067**(0.034) -0.069**(0.033)
年龄 -0.02(0.021) -0.02(0.021) -0.021(0.021) -0.02(0.022)
家庭收入 0.144***(0.021) 0.145***(0.019) 0.143***(0.02) 0.143***(0.02)
外地人 -0.264***(0.048) -0.259***(0.048) -0.265***(0.048) -0.265***(0.046)
单身 -0.313***(0.045) -0.312***(0.044) -0.314***(0.045) -0.311***(0.042)
就业
兼职 0.03(0.06) 0.03(0.06) 0.031(0.061) 0.028(0.06)
家庭主妇 0.224**(0.12) 0.222**(0.117) 0.216**(0.117) 0.212**(0.117)
退休 0.224**(0.119) 0.223**(0.122) 0.235**(0.12) 0.227**(0.121)
待业 -0.068(0.1) -0.068(0.101) -0.067(0.1) -0.066(0.099)
常量 0.892***(0.15) 0.72***(0.116) -0.197(0.194) -0.059(0.226)
城市层级方差 0.096 0.106 0.083 0.077
DIC 7879.02 7880.52 7879.08 7878.36
pD 47.725 48.566 47.127 46.831
Tab.3  自然环境指标的多尺度模型回归结果
变量 模型IX 模型X 模型XI
城市层级
社会治安 2.781***(0.641)
失业率 0.055(2.576)
人文环境 3.088***(0.293)
个体层级
男性 -0.069***(0.035) -0.066**(0.035) -0.065**(0.034)
年龄 -0.022(0.022) -0.016(0.023) -0.022(0.021)
家庭收入 0.143***(0.02) 0.146***(0.02) 0.143***(0.02)
外地人 -0.263***(0.048) -0.261***(0.047) -0.267***(0.047)
单身 -0.318***(0.046) -0.305***(0.046) -0.319***(0.044)
就业
兼职 0.032(0.06) 0.025(0.06) 0.03(0.061)
家庭主妇 0.218**(0.114) 0.218**(0.115) 0.224**(0.115)
退休 0.238**(0.127) 0.224**(0.124) 0.234**(0.123)
待业 -0.07(0.104) -0.066(0.098) -0.069(0.102)
常量 -1.161***(0.439) 0.655***(0.167) -1.377***(0.191)
城市层级方差 0.08 0.111 0.073
DIC 7878.88 7880.71 7877.53
pD 47.077 48.871 45.86
Tab.4  社会人文环境指标的多尺度模型回归结果
变量 模型XII 模型XIII
个体层级
收入 0.145***(0.020)
其它变量 控制 控制
城市层级
总人口(万)
100~200 -0.129(0.108) -0.105(0.110)
200~500 -0.181*(0.120) -0.176*(0.138)
>500 -0.259*(0.182) -0.250*(0.193)
环境健康性 -0.117(0.307) 0.503(0.645)
人文环境 3.082***(0.688) 1.698(1.268)
常量 -1.128***(0.549) -0.661(0.578)
城市层级方差 0.072 0.075
DIC 7942.201 7879.090
pD 46.112 47.427
Tab.5  城市人口规模敏感性检验的多尺度模型回归结果
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