中国榜 · 城邑 China · Cities

全国城市幸福指数City Happiness Index

全部 338 个地级行政区 · 非线性多维幸福模型 · 排名更新日期:2026-10-20All 338 prefecture-level divisions · Non-linear multi-dimensional happiness model · Last updated: 2026-10-20

综述Overview

Happiness is humanity's eternal pursuit and the most comprehensive, most fundamental indicator of a city's development quality. This index synthesizes diverse definitions of "happiness" across time and cultures—Aristotle's "virtue as the highest good", the "Five Blessings" of the Shangshu·Hongfan, Maslow's hierarchy of needs, Seligman's PERMA, the six drivers of the UN World Happiness Report, the Brookings "happiness paradox", and CSIS's six-dimensional well-being—to build a non-linear multi-dimensional happiness model that evaluates all 338 prefecture-level administrative divisions nationwide (including prefecture-level cities, autonomous prefectures, and leagues).
幸福是人类永恒的追求,也是衡量一座城市发展质量最综合、最根本的指标。本指数综合古今中外对"幸福"的多元定义——亚里士多德的"德性即至善"、《尚书·洪范》的"五福"、马斯洛的需求层次、塞利格曼的 PERMA、联合国《世界幸福报告》的六项驱动、Brookings 的"幸福悖论"、CSIS 的六维福祉——构建非线性多维幸福模型,对全国全部 338 个地级行政区(含地级市、自治州、盟)进行评价。
Unlike the province rankings, the city ranking faces the statistical distortion of the "headquarters economy": China's GDP is accounted by the place where legal entities are registered (headquarters), so the output of resource- and production-oriented cities is absorbed by the cities where group headquarters are located. The city ranking therefore introduces an additional "territorial correction coefficient" to restore the true contribution of grassroots cities.
与省市榜不同,城市榜面临"总部经济"的统计扭曲:中国 GDP 按法人注册地(总部)核算,资源、生产型城市的产出被集团总部所在城市吸走。因此城市榜额外引入"属地修正系数",还原基层城市的真实贡献。
Results: Suzhou, Ningbo, Quzhou, Shanghai, and Shenzhen rank in the top five. Overall, cities balanced in health, living comfort, and contribution capacity rank ahead; headquarters cities such as Beijing, Shanghai, Guangzhou, and Shenzhen stand out in contribution, while cities with high-altitude hypoxia and health shortfalls rank behind. This ranking is based mainly on official public data, supplemented by approximations of public knowledge, permanently free and public, for research and reference only.
评价结果:苏州、宁波、衢州、上海、深圳 位列前五。整体看,健康、生活舒适与贡献能力均衡的城市居前;北上广深等总部城市贡献突出,而高原低氧、健康短板的城市靠后。本排名数据以官方公开数据为主、公开知识近似值为辅,永久免费公开,仅供研究与参考。

分层梯队Tiers

A++ 前10 A+ 11-40 A 41-100 B+ 101-200 B 其余Rest

338 城幸福指数排名Happiness Index Ranking of 338 Cities

排名城市省份幸福指数生存系数生活指数贡献指数存款(万)寿命(岁)梯队
1苏州Suzhou江苏44.9162.750.685.21279.19A++
2宁波Ningbo浙江41.7669.048.352.61379.83A++
3衢州Quzhou浙江39.4790.748.532.912.880.17A++
4上海Shanghai上海39.1779.439.670.12482.55A++
5深圳Shenzhen广东38.4983.333.473.31680.0A++
6杭州Hangzhou浙江37.5169.045.738.91679.83A++
7青岛Qingdao山东37.1165.247.940.1979.28A++
8台州Taizhou浙江36.7672.051.123.91079.83A++
9淮安Huai'an江苏36.4282.843.844.49.679.44A++
10广州Guangzhou广东36.271.937.545.01278.97A++
11温州Wenzhou浙江35.7772.047.627.11179.83A+
12金华Jinhua浙江35.1986.546.927.411.779.73A+
13舟山Zhoushan浙江34.885.747.326.511.679.68A+
14珠海Zhuhai广东34.7879.746.637.41278.97A+
15长沙Changsha湖南34.7257.951.736.9777.63A+
16扬州Yangzhou江苏34.6980.943.541.69.379.3A+
17三明Sanming福建34.1585.243.931.96.978.48A+
18湖州Huzhou浙江33.7685.346.824.211.379.58A+
19大连Dalian辽宁33.5562.048.029.3978.23A+
20宿迁Suqian江苏33.5279.743.339.39.179.18A+
21泉州Quanzhou福建33.4868.849.147.6978.09A+
22漳州Zhangzhou福建33.3784.743.630.56.778.36A+
23南京Nanjing江苏33.3459.043.444.61279.19A+
24宁德Ningde福建33.3384.743.530.56.778.35A+
25无锡Wuxi江苏33.1462.749.260.61279.19A+
26成都Chengdu四川32.5954.846.729.6877.1A+
27泰州Taizhou江苏32.4778.742.838.18.979.09A+
28丽水Lishui浙江31.9683.546.321.211.079.45A+
29南通Nantong江苏31.5965.049.546.8979.19A+
30盐城Yancheng江苏31.3677.643.034.98.779.0A+
31嘉兴Jiaxing浙江31.2966.849.940.31179.83A+
32萍乡Pingxiang江西31.1179.752.211.14.977.49A+
33九江Jiujiang江西31.1179.752.211.14.977.49A+
34莆田Putian福建31.0382.742.626.86.378.02A+
35抚州Fuzhou江西30.8279.052.211.04.877.47A+
36佛山Foshan广东30.7264.344.854.11078.97A+
37绍兴Shaoxing浙江30.6666.850.136.91179.83A+
38龙岩Longyan福建30.4881.742.925.56.177.94A+
39本溪Benxi辽宁30.4782.249.311.18.478.6A+
40揭阳Jieyang广东30.3687.734.634.810.279.36A+
41沈阳Shenyang辽宁30.3356.348.224.3978.23A
42威海Weihai山东30.2664.747.016.05.679.09A
43辽阳Liaoyang辽宁30.2181.449.411.08.378.57A
44惠州Huizhou广东30.2187.534.734.010.179.33A
45宜宾Yibin四川30.1773.443.49.76.277.12A
46湘西州Xiangxi Prefecture湖南30.0980.748.713.25.277.96A
47梧州Wuzhou广西30.0481.052.24.83.677.81A
48钦州Qinzhou广西30.0481.052.24.83.677.81A
49玉林Yulin广西30.0381.052.24.83.677.81A
50镇江Zhenjiang江苏29.9576.542.831.58.578.89A
51常州Changzhou江苏29.9359.050.351.51079.19A
52郴州Chenzhou湖南29.7180.648.112.95.277.95A
53铁岭Tieling辽宁29.781.348.710.78.278.53A
54景德镇Jingdezhen江西29.6577.751.410.04.777.31A
55宜春Yichun江西29.6477.751.410.04.777.32A
56湘潭Xiangtan湖南29.6280.448.312.55.277.9A
57厦门Xiamen福建29.6174.245.228.81278.09A
58百色Baise广西29.5980.751.84.53.577.74A
59攀枝花Panzhihua四川29.5469.643.811.46.577.33A
60新余Xinyu江西29.5377.651.49.74.777.29A
61岳阳Yueyang湖南29.5380.448.212.45.177.89A
62鞍山Anshan辽宁29.3280.349.010.18.178.44A
63乌兰察布Ulanqab内蒙古29.0772.250.318.96.177.87A
64南平Nanping福建29.0580.442.622.35.977.78A
65贵港Guigang广西28.9179.551.63.93.477.6A
66日照Rizhao山东28.8872.448.022.66.479.67A
67北海Beihai广西28.8579.451.63.93.477.58A
68阜新Fuxin辽宁28.8480.048.49.78.078.38A
69福州Fuzhou福建28.7870.543.834.6978.09A
70马鞍山Ma'anshan安徽28.7774.450.114.45.477.66A
71滨州Binzhou山东28.7772.348.122.16.379.63A
72安庆Anqing安徽28.7674.450.114.45.477.66A
73北京Beijing北京28.7458.342.863.22982.43A
74连云港Lianyungang江苏28.6775.442.328.88.478.81A
75汕头Shantou广东28.6685.734.631.09.879.18A
76怀化Huaihua湖南28.5778.648.011.65.077.76A
77娄底Loudi湖南28.5478.648.011.65.077.75A
78上饶Shangrao江西28.5375.851.19.04.577.15A
79锦州Jinzhou辽宁28.5279.248.59.47.978.34A
80盘锦Panjin辽宁28.5279.248.59.47.978.34A
81永州Yongzhou湖南28.5278.548.111.35.077.73A
82葫芦岛Huludao辽宁28.5179.148.59.47.978.32A
83衡阳Hengyang湖南28.4478.548.011.35.077.74A
84东莞Dongguan广东28.4366.443.142.31078.97A
85锡林郭勒盟Xilingol League内蒙古28.3769.450.919.56.277.96A
86通辽Tongliao内蒙古28.3570.650.418.46.077.82A
87柳州Liuzhou广西28.2978.551.33.63.377.5A
88崇左Chongzuo广西28.2978.551.33.63.377.5A
89中山Zhongshan广东28.1869.349.522.01078.97A
90河池Hechi广西28.1178.351.23.33.377.46A
91潍坊Weifang山东28.0671.347.521.96.379.61A
92滁州Chuzhou安徽28.0573.549.613.95.377.6A
93昆明Kunming云南27.9563.955.119.6778.5A
94鄂尔多斯Ordos内蒙古27.9169.250.518.76.177.85A
95淮南Huainan安徽27.9173.349.713.45.277.54A
96德州Dezhou山东27.8971.147.621.36.279.56A
97张家界Zhangjiajie湖南27.8377.647.710.84.977.66A
98鹰潭Yingtan江西27.7974.750.88.44.477.04A
99肇庆Zhaoqing广东27.6884.734.329.39.679.08A
100铜陵Tongling安徽27.5972.449.913.15.277.5A
101贺州Hezhou广西27.5677.251.13.03.277.34B+
102乌海Wuhai内蒙古27.3169.049.917.65.977.69B+
103临沂Linyi山东27.0569.947.320.36.179.48B+
104邵阳Shaoyang湖南26.9876.347.49.94.777.51B+
105丹东Dandong辽宁26.9776.947.68.37.578.1B+
106呼伦贝尔Hulunbuir内蒙古26.9568.749.617.25.877.62B+
107韶关Shaoguan广东26.9383.834.128.09.579.01B+
108荆门Jingmen湖北26.975.443.020.27.477.58B+
109黄冈Huanggang湖北26.975.443.020.27.477.58B+
110南宁Nanning广西26.8868.052.412.14.577.47B+
111阳江Yangjiang广东26.7783.634.227.39.478.97B+
112六安Lu'an安徽26.7671.249.512.25.177.38B+
113河源Heyuan广东26.7683.634.227.39.478.97B+
114潮州Chaozhou广东26.7483.734.027.59.478.99B+
115湛江Zhanjiang广东26.6583.634.226.99.478.96B+
116桂林Guilin广西26.6175.450.72.43.177.19B+
117赣州Ganzhou江西26.5772.450.77.04.276.81B+
118南昌Nanchang江西26.5562.750.724.7677.12B+
119江门Jiangmen广东26.5283.534.426.39.378.92B+
120来宾Laibin广西26.5275.250.82.23.177.14B+
121吉安Ji'an江西26.4972.350.66.94.276.79B+
122宣城Xuancheng安徽26.4271.149.111.95.077.35B+
123三沙Sansha海南26.3788.841.35.05.679.34B+
124辽源Liaoyuan吉林26.3279.546.44.87.177.7B+
125防城港Fangchenggang广西26.3275.150.52.23.177.12B+
126益阳Yiyang湖南26.2875.147.09.54.677.39B+
127烟台Yantai山东26.2468.647.518.76.079.35B+
128广元Guangyuan四川26.277.344.012.46.777.49B+
129阜阳Fuyang安徽26.0570.049.311.44.977.26B+
130黄山Huangshan安徽26.0270.149.211.45.077.28B+
131朝阳Chaoyang辽宁25.9774.947.57.57.277.93B+
132抚顺Fushun辽宁25.9675.047.47.67.377.95B+
133营口Yingkou辽宁25.9674.947.57.57.277.93B+
134七台河Qitaihe黑龙江25.9581.945.03.46.778.25B+
135汕尾Shanwei广东25.8982.733.726.19.278.9B+
136梅州Meizhou广东25.8882.733.726.19.278.9B+
137株洲Zhuzhou湖南25.8474.147.28.74.577.3B+
138兴安盟Hinggan League内蒙古25.8366.849.216.35.577.43B+
139荆州Jingzhou湖北25.873.542.818.77.277.44B+
140宜昌Yichang湖北25.7973.542.818.77.277.44B+
141孝感Xiaogan湖北25.7573.542.619.07.277.45B+
142茂名Maoming广东25.7482.534.125.19.178.83B+
143张家口Zhangjiakou河北25.6671.248.59.56.977.54B+
144亳州Bozhou安徽25.6469.249.311.14.977.23B+
145白城Baicheng吉林25.6378.545.94.57.077.61B+
146廊坊Langfang河北25.6271.248.59.56.977.53B+
147通化Tonghua吉林25.678.545.84.57.077.6B+
148衡水Hengshui河北25.5771.148.69.16.877.5B+
149枣庄Zaozhuang山东25.5367.547.118.25.979.32B+
150聊城Liaocheng山东25.5267.547.118.25.979.32B+
151驻马店Zhumadian河南25.4868.349.012.55.077.94B+
152儋州Danzhou海南25.4687.840.64.25.379.1B+
153济源Jiyuan河南25.4568.349.012.55.077.93B+
154三亚Sanya海南25.4587.640.84.05.379.05B+
155赤峰Chifeng内蒙古25.3966.049.315.55.577.37B+
156遂宁Suining四川25.3876.243.611.56.577.36B+
157吴忠Wuzhong宁夏25.3659.858.19.25.476.18B+
158合肥Hefei安徽25.3256.849.733.9777.29B+
159蚌埠Bengbu安徽25.2568.948.810.84.877.16B+
160淮北Huaibei安徽25.2469.048.810.84.877.17B+
161自贡Zigong四川25.2375.543.811.36.577.33B+
162常德Changde湖南25.1773.346.88.04.577.25B+
163德阳Deyang四川25.1475.443.811.16.577.32B+
164大庆Daqing黑龙江25.0480.044.82.76.578.08B+
165佳木斯Jiamusi黑龙江25.0380.044.72.76.578.09B+
166伊春Yichun黑龙江25.0280.044.72.76.578.07B+
167宿州Suzhou安徽24.9568.149.010.54.877.13B+
168承德Chengde河北24.9470.248.09.06.877.47B+
169巴彦淖尔Bayannur内蒙古24.9465.948.615.15.477.34B+
170内江Neijiang四川24.8175.243.310.96.477.28B+
171松原Songyuan吉林24.7976.745.64.06.877.47B+
172东营Dongying山东24.5866.046.916.85.879.18B+
173包头Baotou内蒙古24.5766.148.213.85.377.22B+
174海口Haikou海南24.5484.839.85.8779.1B+
175泸州Luzhou四川24.574.443.410.66.377.23B+
176巴中Bazhong四川24.4874.343.610.26.377.2B+
177运城Yuncheng山西24.4562.152.112.36.177.35B+
178鸡西Jixi黑龙江24.4578.844.52.46.377.94B+
179大兴安岭Daxing'anling Prefecture黑龙江24.3978.744.52.26.377.93B+
180延边州Yanbian Prefecture吉林24.3676.445.13.86.777.4B+
181十堰Shiyan湖北24.2871.142.316.86.977.2B+
182洛阳Luoyang河南24.2766.148.511.54.877.81B+
183濮阳Puyang河南24.2666.148.811.14.877.79B+
184襄阳Xiangyang湖北24.2371.242.116.86.977.22B+
185商丘Shangqiu河南24.2166.148.611.24.877.8B+
186贵阳Guiyang贵州24.1255.457.214.5576.0B+
187晋城Jincheng山西24.0961.352.112.06.177.33B+
188淄博Zibo山东24.0664.747.016.35.779.11B+
189邢台Xingtai河北24.0568.847.77.76.677.31B+
190池州Chizhou安徽24.0466.748.89.14.676.97B+
191天津Tianjin天津23.9260.640.936.311.581.79B+
192绵阳Mianyang四川23.9273.443.49.76.277.12B+
193开封Kaifeng河南23.9166.048.111.04.877.76B+
194平顶山Pingdingshan河南23.965.948.410.64.877.74B+
195新乡Xinxiang河南23.8765.848.410.54.877.73B+
196广安Guang'an四川23.8573.543.19.86.277.14B+
197眉山Meishan四川23.7373.243.29.46.177.09B+
198秦皇岛Qinhuangdao河北23.7167.848.07.36.577.24B+
199阿拉善盟Alxa League内蒙古23.6861.149.116.75.677.49B+
200渭南Weinan陕西23.6465.245.915.77.677.29B+
201咸宁Xianning湖北23.670.141.816.36.877.13B
202雅安Ya'an四川23.672.643.49.36.177.07B
203鄂州Ezhou湖北23.5169.941.916.16.777.08B
204云浮Yunfu广东23.5180.433.719.98.778.63B
205四平Siping吉林23.474.244.93.16.477.19B
206长治Changzhi山西23.3660.151.911.25.977.19B
207吕梁Lüliang山西23.3360.151.911.25.977.18B
208周口Zhoukou河南23.3264.748.49.84.777.64B
209三门峡Sanmenxia河南23.3164.748.49.84.777.64B
210忻州Xinzhou山西23.360.051.811.25.977.17B
211芜湖Wuhu安徽23.2465.648.28.24.576.9B
212恩施州Enshi Prefecture湖北23.2269.042.015.96.677.03B
213白山Baishan吉林23.273.445.12.96.477.15B
214菏泽Heze山东23.1763.346.815.05.578.99B
215清远Qingyuan广东23.1679.633.619.48.678.61B
216南充Nanchong四川23.1272.143.18.76.076.96B
217泰安Tai'an山东23.163.246.714.95.578.97B
218济南Jinan山东23.0951.848.935.5979.28B
219固原Guyuan宁夏23.0755.557.28.25.175.89B
220邯郸Handan河北23.0366.847.56.86.477.17B
221咸阳Xianyang陕西22.9864.145.615.07.577.2B
222铜川Tongchuan陕西22.9764.145.615.07.577.2B
223唐山Tangshan河北22.8966.547.66.36.377.11B
224凉山州Liangshan Prefecture四川22.7368.143.611.56.577.37B
225朔州Shuozhou山西22.7259.251.310.95.877.14B
226焦作Jiaozuo河南22.6863.648.19.24.677.58B
227重庆Chongqing重庆22.6152.637.554.68.477.29B
228吉林Jilin吉林22.5872.344.62.76.277.04B
229鹤岗Hegang黑龙江22.5575.643.21.45.977.63B
230牡丹江Mudanjiang黑龙江22.5275.543.21.45.977.62B
231黑河Heihe黑龙江22.4775.543.21.25.977.62B
232达州Dazhou四川22.4771.042.78.15.876.86B
233黄石Huangshi湖北22.4367.941.714.56.576.94B
234资阳Ziyang四川22.3770.343.08.15.876.84B
235延安Yan'an陕西22.3262.545.814.37.377.1B
236乐山Leshan四川22.2770.142.98.05.876.8B
237沧州Cangzhou河北22.2365.447.35.86.277.01B
238双鸭山Shuangyashan黑龙江22.2174.543.41.05.777.53B
239陇南Longnan甘肃22.2162.551.12.54.476.2B
240黔东南州Qiandongnan Prefecture贵州22.1758.953.44.23.374.84B
241哈密Hami新疆22.1559.449.910.45.376.03B
242安顺Anshun贵州22.1458.853.44.23.374.83B
243济宁Jining山东22.0961.946.313.25.478.9B
244遵义Zunyi贵州22.0758.553.54.13.374.78B
245铜仁Tongren贵州22.0658.753.34.13.374.81B
246鹤壁Hebi河南21.8962.447.68.54.577.5B
247天水Tianshui甘肃21.7861.850.72.54.376.16B
248武汉Wuhan湖北21.7749.445.441.4977.22B
249伊犁州Ili Prefecture新疆21.7758.449.910.55.376.03B
250齐齐哈尔Qiqihar黑龙江21.7673.743.00.95.777.5B
251绥化Suihua黑龙江21.7373.643.00.95.777.47B
252白银Baiyin甘肃21.7361.650.82.34.376.11B
253毕节Bijie贵州21.6957.053.74.73.474.94B
254张掖Zhangye甘肃21.6961.750.72.34.376.13B
255随州Suizhou湖北21.5766.841.313.06.376.84B
256石嘴山Shizuishan宁夏21.5753.456.66.04.775.56B
257长春Changchun吉林21.5456.946.419.5777.34B
258中卫Zhongwei宁夏21.5453.156.56.64.875.62B
259阿克苏Aksu新疆21.558.249.510.25.375.99B
260榆林Yulin陕西21.4660.945.313.77.277.03B
261大同Datong山西21.3956.851.09.45.776.99B
262阳泉Yangquan山西21.3757.051.08.95.676.94B
263石家庄Shijiazhuang河北21.2736.747.316.8777.15B
264保定Baoding河北21.2263.446.95.16.076.9B
265酒泉Jiuquan甘肃21.1660.750.32.04.276.03B
266克拉玛依Karamay新疆21.0957.249.69.95.275.91B
267昌吉州Changji Prefecture新疆21.0857.249.69.95.275.91B
268汉中Hanzhong陕西21.0560.645.112.77.076.88B
269阿勒泰Altay新疆21.0457.249.69.65.275.91B
270晋中Jinzhong山西20.9756.750.58.35.576.87B
271武威Wuwei甘肃20.8759.750.61.74.175.92B
272庆阳Qingyang甘肃20.8759.750.51.84.175.93B
273哈尔滨Harbin黑龙江20.8460.745.610.4777.85B
274安康Ankang陕西20.860.145.212.16.976.83B
275嘉峪关Jiayuguan甘肃20.5558.950.61.54.175.9B
276南阳Nanyang河南20.5159.847.66.14.377.29B
277漯河Luohe河南20.459.647.75.54.377.25B
278信阳Xinyang河南20.3859.647.75.54.377.25B
279西宁Xining青海20.231.155.69.5573.96B
280安阳Anyang河南20.1459.647.25.44.377.24B
281定西Dingxi甘肃20.0658.050.21.24.075.78B
282楚雄州Chuxiong Prefecture云南19.9853.651.56.93.874.61B
283宝鸡Baoji陕西19.9458.544.911.16.876.75B
284海东Haidong青海19.9149.157.25.24.974.14B
285喀什Kashgar新疆19.8555.048.98.95.075.73B
286和田Hotan新疆19.8555.048.98.95.075.72B
287黔西南州Qianxinan Prefecture贵州19.8254.152.62.83.174.45B
288商洛Shangluo陕西19.8258.444.810.96.776.72B
289博州Bortala Prefecture新疆19.8254.948.98.85.075.71B
290临汾Linfen山西19.8154.550.26.75.376.71B
291平凉Pingliang甘肃19.757.549.91.03.975.68B
292许昌Xuchang河南19.6258.447.34.64.277.18B
293黔南州Qiannan Prefecture贵州19.5253.852.42.22.974.27B
294昭通Zhaotong云南19.4352.751.26.43.774.47B
295吐鲁番Turpan新疆19.2454.148.48.24.875.57B
296金昌Jinchang甘肃19.256.749.40.73.975.61B
297怒江州Nujiang Prefecture云南19.1952.750.56.33.674.47B
298大理州Dali Prefecture云南18.9751.551.26.43.774.49B
299郑州Zhengzhou河南18.9444.047.832.8677.57B
300临夏州Linxia Prefecture甘肃18.8256.249.10.43.775.49B
301巴州Bayingolin Prefecture新疆18.7752.848.68.04.875.53B
302文山州Wenshan Prefecture云南18.6151.550.65.53.574.29B
303呼和浩特Hohhot内蒙古18.5249.048.914.4677.56B
304玉溪Yuxi云南18.4451.250.55.33.574.25B
305克州Kizilsu Prefecture新疆18.4352.448.07.84.775.46B
306红河州Honghe Prefecture云南18.3150.650.75.33.574.22B
307六盘水Liupanshui贵州18.2650.352.32.22.974.27B
308甘孜州Garzê Prefecture四川17.8154.843.29.86.277.15B
309黄南州Huangnan Prefecture青海17.8143.657.55.45.074.21B
310西双版纳州Xishuangbanna Prefecture云南17.6949.350.54.83.474.08B
311塔城Tacheng新疆17.6851.147.76.84.675.33B
312丽江Lijiang云南17.4848.350.75.33.574.22B
313甘南州Gannan Prefecture甘肃17.3551.349.60.43.875.57B
314德宏州Dehong Prefecture云南17.3449.049.94.63.374.04B
315海南州Hainan Prefecture青海17.3443.356.74.64.774.0B
316曲靖Qujing云南17.1948.450.04.73.374.01B
317临沧Lincang云南17.1248.150.24.63.373.97B
318海北州Haibei Prefecture青海17.041.757.55.24.974.18B
319阿坝州Ngawa Prefecture四川16.8553.742.57.85.776.76B
320保山Baoshan云南16.7647.749.54.53.273.92B
321普洱Pu'er云南16.6747.349.74.53.273.91B
322银川Yinchuan宁夏16.2738.156.912.65.575.88B
323乌鲁木齐Urumqi新疆15.6938.950.119.8675.68B
324徐州Xuzhou江苏15.1440.943.124.0679.19B
325海西州Haixi Prefecture青海14.4536.156.74.64.774.0B
326兰州Lanzhou甘肃14.436.752.311.5675.83B
327太原Taiyuan山西14.3134.652.318.3777.1B
328西安Xi'an陕西14.2635.645.129.5877.01B
329迪庆州Diqing Prefecture云南13.7837.950.56.33.674.47B
330果洛州Golog Prefecture青海12.5531.356.74.94.774.01B
331林芝Nyingchi西藏12.1230.357.04.04.771.86B
332玉树州Yushu Prefecture青海11.9329.357.35.65.074.27B
333昌都Qamdo西藏11.5428.857.13.84.871.96B
334山南Shannan西藏10.9527.157.44.14.972.07B
335拉萨Lhasa西藏9.7323.258.17.9472.19B
336日喀则Shigatse西藏9.022.357.44.25.072.12B
337阿里Ngari西藏8.6921.557.44.25.072.12B
338那曲Nagqu西藏8.4921.357.03.84.871.91B

算法详细论述Methodology

一、幸福的多维定义与理论依据

This algorithm does not presuppose a single definition of "happiness", but instead synthesizes diverse perspectives from across time and cultures and maps them onto four tiers of the model:
本算法不预设单一的"幸福"定义,而是综合古今中外的多元视角,将其映射到模型的四个层次:
  1. 亚里士多德《尼各马可伦理学》:幸福(eudaimonia)是灵魂合乎德性的现实活动,是人生的终极目的(至善),而非享乐——为"贡献维度"提供哲学依据。
  2. 《尚书·洪范》"五福":长寿、富贵、康宁、好德、善终——"长寿""康宁"为"生存系数(健康)"提供依据。
  3. 马斯洛需求层次理论:生理、安全、归属、尊重、自我实现——为"物质→社会→贡献"的递进结构提供依据。
  4. 积极心理学 PERMA(塞利格曼):积极情绪、投入、关系、意义、成就——“关系”对应社会维度,“意义”对应贡献维度。
  5. 联合国《世界幸福报告》:以坎特里尔阶梯量表测量生活满意度,识别人均 GDP、社会支持、健康寿命、自由、慷慨、清廉六项驱动因素。
  6. Brookings 幸福研究(Carol Graham,2010):"幸福悖论"——幸福取决于适应与期望而非绝对收入——决定"收入取对数"的非线性处理。
  7. CSIS《全球青年福祉指数》(2014):公民参与、教育、经济机会、健康、安全、信息通信六维——为多维框架提供参照。

二、模型的数学结构

生存系数 S = (寿命′ × 空气′ × 含氧′)1/3
The geometric mean of the three health components: any health shortfall (high-altitude hypoxia, short life expectancy) will drag down the whole, reflecting the "bucket effect".
健康三要素几何平均:任何一个健康短板(高原低氧、寿命短)都会把整体压低,体现"木桶效应"。
生活指数 W = 0.20·log₂(1+收入′) + 0.20·存款′ + 0.20·房价比′ + 0.20·婚姻′ + 0.20·拥堵′
Income is log-transformed: per the Easterlin paradox and the Brookings happiness paradox, the contribution of income to happiness diminishes at the margin.
收入取对数:依据 Easterlin 悖论与 Brookings 幸福悖论,收入对幸福的贡献边际递减。
贡献指数 C = λ × (0.25·第二产业′ + 0.30·人均GDP′ + 0.20·GDP′ + 0.15·税收′ + 0.10·研发′)
Here λ is the territorial correction coefficient (see Section 4).
其中 λ 为属地修正系数(见第四节)。
幸福指数 = 100 × [ S × (0.70·W + 0.30·C) + 0.10·舆论′ ]
The survival coefficient S acts as a multiplier (health is a hard threshold); living W and contribution C are additive (mutually compensable); public opinion accounts for only 10%.
生存系数 S 作为乘数(健康是硬门槛);生活 W 与贡献 C 相加(可互补);舆论仅占 10%。

三、指标与权重依据

指标方向权重所属层依据
平均预期寿命正向生存健康五福之长寿;世界幸福报告之健康寿命
空气质量正向生存健康五福之康宁;环境健康
空气含氧量正向生存健康高原低氧与宜居幸福相悖的地域修正
人均可支配收入对数生活 0.20物质Easterlin 悖论;边际递减
人均存款正向生活 0.20物质家底/抗风险能力(存量财富)
房价收入比反向生活 0.20物质居住成本是生活舒适的核心
婚姻比例正向生活 0.20社会PERMA 之关系;世界幸福报告之社会支持
交通拥堵反向生活 0.20便利生活便利/通勤质量
第二产业增加值正向贡献 0.25贡献属地生产;修正总部经济扭曲
人均 GDP正向贡献 0.30贡献人均创造价值,公平化中小城市
GDP 总量正向贡献 0.20贡献经济总产出
财政收入(税收)正向贡献 0.15贡献财政贡献
研发经费正向贡献 0.10贡献科技创新贡献

四、总部经济修正(属地修正系数 λ)

China's GDP is accounted by the place where legal entities are registered (headquarters), producing the distortion of "production at the grassroots, statistics at the headquarters". A typical case is resource-based industrial cities: the output of reservoirs, mines, and factories is produced on site, but the value added and income are recorded in the city where the group headquarters is located.
中国 GDP 按法人注册地(总部)核算,导致"生产在基层、统计在总部"的扭曲。典型如资源型工业城市:水库、矿山、工厂的产出在生产地,增加值与收入却记在集团总部所在的城市。
Correction method: ① the Contribution dimension centers on the value added of the secondary industry (territorial production)—wherever factories, mines, and water sources are located, the output value is attributed there; ② a territorial correction coefficient λ = secondary-industry share ÷ national average (40%) is introduced and capped within the [0.75, 1.45] interval. The higher a city's secondary-industry share (industrial and resource-based cities), the stronger its territorial production and the more it is absorbed by headquarters, so λ is larger and its contribution is adjusted upward more; headquarters/service cities have λ below 1 and their contribution is adjusted downward accordingly. This correction restores the true contribution of grassroots resource- and industry-oriented cities.
修正方法:①贡献维度以第二产业增加值(属地生产)为核心,工厂、矿山、水源在哪,产值就归属哪;②引入属地修正系数 λ=第二产业占比 ÷ 全国均值(40%),并限定在 [0.75, 1.45] 区间。第二产业占比越高的城市(工业型、资源型),属地生产越强、被总部吸走越多,λ 越大、贡献上调越多;总部/服务业城市 λ 小于 1、贡献相应下调。这一修正还原了基层资源型、工业型城市的真实贡献。

五、数据来源与局限性

Income, average life expectancy, GDP, tax revenue, R&D, and value added of the secondary industry use official public data (annual data from the National Bureau of Statistics and the Seventh National Population Census); per-capita deposits, house-price-to-income ratio, air quality, traffic congestion, marriage ratio, and per-capita GDP are approximations of public knowledge, marked "subject to official figures"; provincial capitals and economically strong cities (50 cities) use a fine-grained scope, while the remaining prefecture-level cities are fine-tuned on a provincial baseline by city size with deterministic perturbations added; the public-opinion layer adopts Xinhua's Liaowang Dongfang Weekly "2024 Happiest Cities". This model does not include subjective happiness that is hard to quantify, and approximations for small and medium cities carry some error; the ranking is for research and reference only.
收入、平均预期寿命、GDP、税收、研发、第二产业增加值采用官方公开数据(国家统计局年度数据、第七次全国人口普查);人均存款、房价收入比、空气质量、交通拥堵、婚姻比例、人均 GDP 为公开知识近似值,标注"以官方为准";省会及经济强市(50 城)用精细口径,其余地级市在省份基准上按城市规模微调并加确定性扰动;舆论层采用新华社《瞭望东方周刊》"2024 最具幸福感城市"。本模型不包含难以量化的主观幸福感,且中小城市近似值存在误差,排名仅供研究与参考。

六、参考文献

  1. 亚里士多德:《尼各马可伦理学》(Eudaimonia 与德性伦理)。
  2. 《尚书·洪范》:"五福"(长寿、富贵、康宁、好德、善终)。
  3. 边沁《道德与立法原理导论》、密尔《功利主义》:最大多数人的最大幸福。
  4. 马斯洛《动机与人格》:需求层次理论。
  5. 塞利格曼《持续的幸福》:PERMA 模型。
  6. Helliwell, J., Layard, R., & Sachs, J.(编):《World Happiness Report》,联合国可持续发展解决方案网络(SDSN)。
  7. Graham, C.(2010):"Adaptation amidst Prosperity and Adversity: Insights from Happiness Studies from Around the World",Brookings Institution。
  8. Goldin, N.(2014):"The Global Youth Wellbeing Index",CSIS & International Youth Foundation。
  9. Easterlin, R.(1974):"Does Economic Growth Improve the Human Lot? Some Empirical Evidence",Nations and Households in Economic Growth。
  10. 国家统计局:年度国民经济与社会发展统计公报。
  11. 国务院第七次全国人口普查领导小组办公室:第七次全国人口普查数据。
  12. 新华社《瞭望东方周刊》:"2024 最具幸福感城市"调查推选结果。

说明Notes

本榜与省市幸福指数同标准,采用非线性多维幸福模型(非简单线性加权):①生存系数 S=(寿命×空气×含氧)1/3,健康三要素几何平均,体现短板/互斥效应;②生活指数 W=0.20·log₂(1+收入)+0.20·存款+0.20·房价比+0.20·婚姻+0.20·拥堵,收入取对数体现边际递减(Easterlin 悖论);③贡献指数 C=属地修正系数×[0.25·第二产业(属地生产)+0.30·人均GDP+0.20·GDP+0.15·税收+0.10·研发],其中属地修正系数=第二产业占比÷全国均值(40%),修正"总部经济"扭曲——工厂矿山水源在生产地、增加值却记在总部城市;④综合幸福指数=100×[S×(0.70W+0.30C)+0.10×舆论]。省会及经济强市用精细口径,其余地级市在省份基准上微调并加扰动。数据:收入、寿命、GDP、税收、研发为官方公开数据,其余为公开知识近似值,以官方为准。
This ranking shares the same standard as the provincial/municipal happiness index and adopts a nonlinear multidimensional happiness model (not simple linear weighting): ① Survival coefficient S=(lifespan×air×oxygen)1/3, the geometric mean of the three health factors, reflecting short-board/mutual-exclusion effects; ② Living index W=0.20·log₂(1+income)+0.20·savings+0.20·housing-price ratio+0.20·marriage+0.20·congestion, with income log-transformed to reflect diminishing marginal returns (the Easterlin paradox); ③ Contribution index C=locality correction factor×[0.25·secondary industry (local production)+0.30·GDP per capita+0.20·GDP+0.15·tax revenue+0.10·R&D], where the locality correction factor = secondary industry share ÷ national average (40%), correcting the "headquarters economy" distortion — factories, mines, and water sources lie at the production sites while value added is recorded in the headquarters city; ④ Composite happiness index = 100×[S×(0.70W+0.30C)+0.10×public opinion]. Provincial capitals and economically strong cities use a refined basis, while other prefecture-level cities are fine-tuned on the provincial benchmark with added perturbation. Data: income, lifespan, GDP, tax revenue, and R&D are official public data; the rest are public approximate values, subject to official figures.