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城市竞争力排名City Competitiveness

50 个主要城市 · 八维加权模型(经济体量取对数) · 排名更新日期:2026-10-2050 major cities · Eight-dimensional weighted model (log of economic size) · Last updated: 2026-10-20

综述Overview

Competitiveness is the core indicator for measuring a city's comprehensive capability in resource allocation, value creation, and sustained growth. Unlike subjective experiences such as "happiness", urban competitiveness is closer to an objective, measurable concept of "productivity"—whether a city can more efficiently aggregate capital, talent, and technology and convert them into sustainable economic output.
竞争力是衡量一座城市在资源配置、价值创造与持续增长方面综合能力的核心指标。与"幸福"这类主观体验不同,城市竞争力更接近一个客观、可度量的"生产力"概念——一座城市能否更高效地集聚资本、人才与技术,并转化为可持续的经济产出。
This index synthesizes mainstream international competitiveness theories—Michael Porter's theory of national competitive advantage, the pillar framework of the World Economic Forum (WEF) Global Competitiveness Report, and the factor structure of the IMD World Competitiveness Yearbook—to build an eight-variable weighted model that evaluates 50 major cities nationwide across four tiers: economic scale, per-capita output, fiscal and innovation, and talent reserves.
本指数综合国际主流的竞争力理论——迈克尔·波特的国家竞争优势理论、世界经济论坛(WEF)《全球竞争力报告》的支柱框架、瑞士洛桑国际管理发展学院(IMD)《世界竞争力年鉴》的要素结构——构建八维加权模型,从经济体量、人均产出、财政与创新、人才储备四个层次对全国 50 个主要城市进行评价。
Economic competitiveness has a markedly "near-linear" character: the larger the economic scale and output, the higher competitiveness usually is. Yet to compress the order-of-magnitude gaps—often dozens of times—between megacities and small cities, this model applies the logarithm uniformly to GDP-type scale indicators, so the ranking reflects size advantages without being entirely dominated by extreme values.
经济竞争力具有显著的"偏线性"特征:经济体量与产出规模越大,竞争力通常越高;但同时为压缩超大城市与小城市之间动辄数十倍的量级差异,本模型对 GDP 类规模指标统一取对数处理,使排名既体现体量优势,又不被极端值完全主导。
Results: Beijing, Shanghai, Shenzhen, Guangzhou, and Suzhou rank in the top five. Overall, cities with large economic scale, high per-capita output, strong fiscal and innovation capacity, and ample university talent reserves rank ahead, while cities that are small in scale or weak in innovation 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:北京、上海、深圳、广州、苏州 位列前五。整体看,经济体量大、人均产出高、财政与创新能力强、高校人才储备充足的城市居前;体量小或创新薄弱的城市靠后。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++ 前三Top 3 A+ 四至十4th–10th A 十一至二十11th–20th B+ 二十一至三十五 B 其余Rest

50 城竞争力排名Competitiveness Ranking of 50 Cities

排名城市省份竞争力指数经济体量人均产出财政创新人才储备GDP(亿)存款(亿)高校(所)梯队
1北京Beijing北京97.044.715.029.38.04376028000092A++
2上海Shanghai上海94.5146.113.929.15.44721822000064A++
3深圳Shenzhen广东87.2343.715.027.70.83460613000014A++
4广州Guangzhou广东83.0340.710.724.57.2303569000083A+
5苏州Suzhou江苏78.6739.512.924.51.8246534500025A+
6杭州Hangzhou浙江76.3237.210.724.63.9200597000047A+
7南京Nanjing江苏75.7335.712.923.04.2174215000051A+
8武汉Wuhan湖北74.236.08.622.57.2200113500083A+
9重庆Chongqing重庆73.3340.63.623.35.8301455000068A+
10无锡Wuxi江苏69.2333.613.921.00.6154562200012A+
11成都Chengdu四川68.836.74.322.94.9220745000058A
12天津Tianjin天津68.7735.16.422.64.7167374200056A
13宁波Ningbo浙江68.334.710.721.91.0164523000016A
14长沙Changsha湖南65.832.87.520.84.8143312500057A
15青岛Qingdao山东64.7933.58.620.91.8157602500025A
16郑州Zhengzhou河南63.5132.64.320.56.1136182600071A
17济南Jinan山东62.7631.77.520.23.4127572300042A
18合肥Hefei安徽62.6231.56.420.14.5126732200054A
19佛山Foshan广东61.8232.77.520.80.7132762000013A
20西安Xi'an陕西60.931.93.220.45.3120102800063A
21福州Fuzhou福建60.6531.47.519.02.8129282000035B+
22常州Changzhou江苏60.6529.711.818.60.6101161600011B+
23南通Nantong江苏57.2831.27.518.30.311813170008B+
24东莞Dongguan广东56.331.24.320.40.411438180009B+
25泉州Quanzhou福建55.9430.86.417.51.2121721200018B+
26厦门Xiamen福建54.8527.28.618.11.080661800016B+
27沈阳Shenyang辽宁53.4828.23.218.43.781222100045B+
28大连Dalian辽宁53.3928.64.318.22.387521800030B+
29绍兴Shaoxing浙江52.027.26.417.80.677911300011B+
30南昌Nanchang江西51.5826.15.415.64.573241400054B+
31嘉兴Jiaxing浙江51.4927.26.417.60.37062150008B+
32温州Wenzhou浙江50.1628.33.218.10.687301600011B+
33昆明Kunming云南49.5326.93.216.23.278641700040B+
34珠海Zhuhai广东49.0621.611.815.20.542331100010B+
35石家庄Shijiazhuang河北48.5926.91.117.13.675341800044B+
36长春Changchun吉林47.9625.92.116.63.370001400041B
37徐州Xuzhou江苏47.0827.62.116.70.689001200012B
38太原Taiyuan山西45.1624.42.114.83.855711500046B
39台州Taizhou浙江44.7324.83.216.70.06240110005B
40哈尔滨Harbin黑龙江44.0623.80.016.34.055761600049B
41贵阳Guiyang贵州41.5222.72.113.92.851551200035B
42南宁Nanning广西41.0522.91.114.32.854691200035B
43乌鲁木齐Urumqi新疆39.1621.43.212.91.740601100023B
44中山Zhongshan广东38.021.52.114.30.13625100006B
45呼和浩特Hohhot内蒙古35.7919.33.211.61.73802800024B
46兰州Lanzhou甘肃34.2219.21.111.92.03487900027B
47银川Yinchuan宁夏28.0216.22.18.90.82685600014B
48海口Haikou海南22.7612.41.18.80.62134700011B
49西宁Xining青海21.5812.21.17.80.51700500010B
50拉萨Lhasa西藏2.140.02.10.00.050025005B

算法详细论述Methodology

一、竞争力的定义与理论依据

This algorithm does not presuppose a single definition of "competitiveness", but instead synthesizes mainstream international competitiveness research frameworks and maps them onto four tiers of the model:
本算法不预设单一的"竞争力"定义,而是综合国际主流竞争力研究框架,将其映射到模型的四个层次:
  1. 迈克尔·波特《国家竞争优势》(1990):竞争力的本质是"生产率"——一国或一地在特定产业中创造更高价值的能力,取决于要素条件、需求条件、相关与支持产业、企业战略与竞争四大要素("钻石模型")——为"经济体量+财政创新+人才储备"的结构提供依据。
  2. 迈克尔·波特《竞争战略》(1980):竞争优势来源于成本领先、差异化与聚焦——为"人均产出(效率)"维度提供依据。
  3. 世界经济论坛(WEF)《全球竞争力报告》:以制度、基础设施、宏观经济、健康、教育、市场效率、创新等支柱衡量竞争力——为多维指标体系提供参照。
  4. 瑞士洛桑国际管理发展学院(IMD)《世界竞争力年鉴》:以经济表现、政府效率、企业效率、基础设施四要素衡量竞争力——为"财政与创新"维度提供依据。
  5. 城市经济学与集聚理论:城市竞争力源于要素集聚带来的规模经济与溢出效应——决定"经济体量"维度权重最高。

二、模型的数学结构

The city competitiveness index uses a weighted-sum model, with the four tier weights summing to 1:
城市竞争力指数采用加权求和模型,四大层次权重之和为 1:
经济体量 E = 0.20·ln(GDP)′ + 0.15·ln(第二产业)′ + 0.12·ln(金融机构存款)′ 人均产出 P = 0.15·人均GDP′ 财政与创新 I = 0.12·ln(税收)′ + 0.10·ln(研发经费)′ + 0.08·ln(专利)′ 人才储备 T = 0.08·高校数′ 竞争力指数 = 100 × ( E + P + I + T )
Here, indicators marked with ′ are normalized (min-max normalized to [0,1]); ln indicates that scale-type indicators are log-transformed (natural logarithm) before normalization. "Scale/volume-type" indicators such as GDP, value added of the secondary industry, deposits in financial institutions, tax revenue, R&D expenditure, and patents are log-transformed, while "ratio/quantity-type" indicators such as per-capita GDP and the number of universities are linearly normalized directly.
其中,带 ′ 者为归一化后的指标(min-max 归一到 [0,1]);ln 表示对规模类指标取自然对数后再归一化。GDP、第二产业增加值、金融机构存款、税收、研发经费、专利等"规模/体量类"指标取对数,人均 GDP 与高校数等"比例/数量类"指标直接线性归一。

三、对数处理的依据

A key difference between economic competitiveness and happiness is that happiness exhibits "diminishing marginal returns" (the Easterlin paradox), whereas economic competitiveness is essentially near-linear—the larger the economy and the higher the output, the stronger competitiveness usually is. This model therefore assigns relatively high weights to GDP-type indicators to reflect the basic law that "scale determines competitiveness".
经济竞争力与幸福感的一个关键区别在于:幸福感存在"边际递减"(Easterlin 悖论),而经济竞争力本质上是偏线性的——经济体量越大、产出越高,竞争力通常越强。因此本模型对 GDP 类指标采用较高的权重,以体现"体量决定竞争力"的基本规律。
But pure linearity also creates problems: between the largest and smallest cities, indicators such as GDP, deposits, and patents can differ by dozens or even hundreds of times. If normalized directly and linearly, extreme values would compress all other cities to near zero and erase differentiation. Scale-type indicators are therefore uniformly log-transformed, which preserves the ordering advantage of size while compressing magnitude differences and making the ranking distribution more balanced and robust.
但纯粹线性也会带来问题:最大的城市与最小的城市之间,GDP、存款、专利等指标可相差数十倍乃至数百倍,若直接线性归一,极端值会把其余城市全部压缩到接近 0,丧失区分度。故对规模类指标统一取自然对数,在保留体量排序优势的同时,压缩量级差异,使排名分布更为均衡、稳健。

四、指标与权重依据

指标处理权重所属层依据
GDP 总量对数0.20经济体量经济总产出,竞争力的核心载体
第二产业增加值对数0.15经济体量工业与实体经济的生产能力
金融机构存款对数0.12经济体量资本集聚与金融资源动员能力
人均 GDP线性0.15人均产出劳动生产率与财富创造效率
财政收入(税收)对数0.12财政与创新财政汲取与公共资源配置能力
研发经费对数0.10财政与创新科技创新投入强度
专利拥有量对数0.08财政与创新创新产出与成果转化能力
高校数线性0.08人才储备人力资本供给与人才培养能力
The four tier weights total: economic scale 0.47, per-capita output 0.15, fiscal and innovation 0.30, and talent reserves 0.08, summing to 1.
四层权重合计:经济体量 0.47、人均产出 0.15、财政与创新 0.30、人才储备 0.08,总和为 1。

五、数据来源与局限性

GDP, per-capita GDP, fiscal revenue (tax), R&D expenditure, and value added of the secondary industry directly follow the official public data scope used by the CPPPS City Happiness Index (annual data from the National Bureau of Statistics and each city's statistical bulletin, 2023 approximations); the balance of deposits in financial institutions, the number of regular higher-education institutions, and the number of valid invention patents are approximations of public knowledge, all marked "subject to official figures". This model focuses on economic competitiveness and does not include hard-to-quantify soft factors such as institutional environment, business environment, and infrastructure; approximations carry some error, and the ranking is for research and reference only.
GDP、人均 GDP、财政收入(税收)、研发经费、第二产业增加值直接沿用 CPPPS 城市幸福指数所用官方公开数据口径(国家统计局年度数据及各城市统计公报,2023 年近似值);金融机构存款余额、普通高校数、有效发明专利拥有量为公开知识近似值,均标注"以官方为准"。本模型聚焦经济竞争力,未纳入制度环境、营商环境、基础设施等难以量化的软性因素,且近似值存在误差,排名仅供研究与参考。

六、参考文献

  1. Porter, M. E.(1990):《The Competitive Advantage of Nations》,Free Press(国家竞争优势理论 / 钻石模型)。
  2. Porter, M. E.(1980):《Competitive Strategy: Techniques for Analyzing Industries and Competitors》,Free Press。
  3. World Economic Forum(WEF):《Global Competitiveness Report》,各年度。
  4. IMD World Competitiveness Center:《World Competitiveness Yearbook》,各年度。
  5. Krugman, P.(1991):"Increasing Returns and Economic Geography",Journal of Political Economy(新经济地理与集聚)。
  6. Easterlin, R.(1974):"Does Economic Growth Improve the Human Lot?",Nations and Households in Economic Growth(作为与幸福模型对照的边际递减参考)。
  7. 国家统计局:年度国民经济与社会发展统计公报。
  8. 各城市统计年鉴与国民经济和社会发展统计公报(2023 年)。

说明Notes

本榜采用八维加权模型,衡量城市经济竞争力:①经济体量 E=0.20·ln(GDP)+0.15·ln(第二产业)+0.12·ln(金融机构存款),权重最高,体现"体量决定竞争力";②人均产出 P=0.15·人均GDP,衡量劳动效率;③财政与创新 I=0.12·ln(税收)+0.10·ln(研发)+0.08·ln(专利),衡量创新动能;④人才储备 T=0.08·高校数,衡量人力资本供给。竞争力指数=100×(E+P+I+T)。其中规模类指标取自然对数以压缩量级差异、避免极端值主导,比例与数量类指标线性归一。数据:GDP、人均GDP、税收、研发、第二产业为官方公开数据(2023 近似),金融机构存款、高校数、专利数为公开知识近似值,均以官方为准。
This ranking adopts an eight-dimension weighted model to measure urban economic competitiveness: ① Economic size E=0.20·ln(GDP)+0.15·ln(secondary industry)+0.12·ln(financial institution deposits), with the highest weight, reflecting that "size determines competitiveness"; ② Output per capita P=0.15·GDP per capita, measuring labor efficiency; ③ Fiscal and innovation I=0.12·ln(tax revenue)+0.10·ln(R&D)+0.08·ln(patents), measuring innovation momentum; ④ Talent reserve T=0.08·number of universities, measuring human capital supply. Competitiveness index = 100×(E+P+I+T). Scale-type indicators take natural logarithms to compress magnitude differences and avoid extreme values dominating, while ratio and count indicators are linearly normalized. Data: GDP, GDP per capita, tax revenue, R&D, and secondary industry are official public data (2023 approximation); financial institution deposits, number of universities, and patent counts are public approximate values, all subject to official figures.