The challenges faced by cities in Asia and China are changing rapidly. They’re not only more complex, but specific to each place. We have created the SmartEcoCity Index (SECI) to track the performance of Chinese cities. The index provides granular level data on behavioural habits and government investments from cities in Asia.
We analysed 100 cities in Asia for a total of 24 factors, and ranked the top 20 to determine the cities that manage their assets and resources most efficiently.
Smart (Equity)
- Smartphone penetration
- Internet penetration
- Cable TV penetration
- Smart Parking
- Sharing Services
- Traffic
- Public Transport
- Smartness opinion
- Skyscrapers
Eco (Environment)
- Clean Energy/EV Incentives
- Smart Building
- Waste Disposal
- Environment Protection
- Afordability
- Medical Care
- Environmental exposure
City (Economy)
- Leadership
- Budget
- Urban Planning
- Cityzen Participation
- Digitalization of government
- Industries
- Business eco-system
- Education
| Transport and mobility | Sustainability | Governance | Innovation Economy | Digitalization | Living Standard | Expert Perception | |||||||||||||||||||||
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SECI |
| 1 | Shenzhen | China | 5.13 | 7.13 | 5.01 | 8.56 | 8.87 | 4.18 | 9.92 | 1.76 | 8.25 | 6.14 | 6.49 | 8.22 | 7.43 | 7.35 | 5.42 | 5.31 | 3.82 | 9.85 | 9.47 | 5.99 | 9.16 | 9.09 | 9.55 | 9.16 | 7.63 |
| 2 | Shanghai | China | 5.13 | 6.22 | 9.62 | 9.47 | 7.96 | 4.18 | 9.92 | 1.76 | 8.25 | 7.96 | 6.49 | 7.59 | 8.71 | 5.54 | 8.34 | 8.03 | 1.78 | 9.70 | 8.94 | 4.40 | 9.16 | 9.09 | 9.92 | 6.57 | 7.57 |
| 3 | Beijing | China | 9.91 | 8.87 | 8.87 | 3.34 | 8.26 | 5.99 | 1.92 | 6.82 | 8.93 | 4.93 | 5.19 | 9.22 | 9.55 | 8.41 | 5.46 | 6.97 | 9.22 | 8.18 | 6.75 | 6.97 | 6.49 | 7.28 | 7.43 | 10.00 | 7.55 |
| 4 | Hangzhou | China | 9.25 | 7.35 | 8.71 | 5.76 | 5.99 | 7.88 | 8.54 | 6.75 | 8.25 | 6.75 | 9.31 | 9.46 | 9.24 | 8.79 | 6.14 | 7.73 | 3.43 | 5.54 | 4.55 | 3.34 | 9.69 | 7.20 | 6.14 | 10.00 | 7.38 |
| 5 | Guangzhou | China | 8.22 | 7.88 | 6.37 | 4.10 | 6.82 | 3.34 | 8.92 | 5.01 | 8.70 | 6.90 | 8.93 | 9.22 | 10.00 | 7.50 | 5.05 | 5.39 | 3.27 | 6.82 | 7.05 | 5.08 | 8.02 | 7.73 | 7.81 | 10.00 | 7.29 |
| 6 | Tianjin | China | 6.44 | 9.55 | 5.76 | 7.35 | 9.92 | 5.92 | 9.23 | 4.40 | 3.82 | 2.36 | 4.43 | 3.33 | 6.75 | 9.85 | 4.03 | 4.86 | 7.50 | 9.32 | 8.87 | 8.34 | 7.33 | 9.92 | 5.84 | 9.16 | 7.22 |
| 7 | Chongqing | China | 5.13 | 8.49 | 4.63 | 6.14 | 8.18 | 1.53 | 9.54 | 5.92 | 10.00 | 4.18 | 8.40 | 8.60 | 1.98 | 7.81 | 3.61 | 3.80 | 7.03 | 8.94 | 8.18 | 7.20 | 10.00 | 7.50 | 8.64 | 9.16 | 7.17 |
| 8 | Chengdu | China | 7.38 | 2.82 | 9.47 | 4.78 | 4.63 | 7.88 | 8.54 | 6.75 | 8.25 | 9.17 | 9.31 | 9.38 | 8.41 | 4.55 | 8.98 | 9.09 | 1.31 | 7.96 | 7.35 | 2.82 | 9.69 | 7.20 | 8.56 | 6.57 | 7.14 |
| 9 | Singapore* | Singapore | 6.44 | 6.90 | 3.80 | 8.79 | 7.05 | 1.76 | 1.46 | 6.37 | 9.08 | 2.13 | 2.68 | 10.00 | 3.27 | 3.34 | 1.19 | 3.42 | 7.65 | 9.92 | 10.00 | 9.09 | 9.77 | 10.00 | 8.34 | 10.00 | 7.07 |
| 10 | Nanjing | China | 9.25 | 8.41 | 6.37 | 6.52 | 6.90 | 9.77 | 2.46 | 9.02 | 5.81 | 4.78 | 4.05 | 3.79 | 5.01 | 4.71 | 7.43 | 8.18 | 9.69 | 5.39 | 8.41 | 5.46 | 7.03 | 6.22 | 8.87 | 9.16 | 7.07 |
| 11 | Wuhan | China | 7.38 | 7.58 | 4.63 | 7.73 | 3.04 | 4.33 | 8.92 | 7.50 | 8.93 | 8.87 | 10.00 | 2.01 | 8.64 | 4.86 | 7.20 | 8.71 | 4.05 | 8.87 | 9.02 | 3.12 | 7.79 | 7.96 | 9.62 | 6.57 | 7.05 |
| 12 | Taipei* | Taiwan | 5.13 | 1.61 | 2.36 | 8.26 | 8.11 | 4.40 | 1.62 | 4.10 | 9.16 | 2.44 | 4.51 | 5.97 | 7.50 | 9.62 | 2.97 | 3.04 | 3.58 | 9.09 | 8.26 | 6.67 | 7.41 | 7.35 | 9.17 | 9.16 | 6.57 |
| 13 | Tokyo* | Japan | 8.22 | 8.79 | 2.36 | 8.11 | 10.00 | 9.24 | 4.00 | 8.49 | 3.44 | 1.61 | 5.04 | 2.47 | 6.52 | 6.22 | 2.17 | 2.13 | 1.47 | 1.83 | 3.27 | 9.77 | 1.76 | 9.32 | 3.95 | 10.00 | 5.88 |
| 14 | Osaka* | Japan | 2.78 | 8.94 | 7.28 | 6.67 | 8.49 | 9.24 | 4.00 | 8.49 | 3.44 | 2.82 | 5.04 | 2.47 | 7.05 | 6.14 | 3.84 | 2.74 | 1.47 | 1.91 | 3.65 | 6.29 | 1.76 | 9.32 | 5.01 | 9.16 | 5.77 |
| 15 | Abu Dhabi* | UAE | 6.44 | 4.93 | 9.02 | 9.02 | 6.37 | 8.03 | 1.15 | 2.36 | 2.60 | 2.74 | 1.69 | 1.08 | 7.28 | 7.66 | 4.25 | 4.48 | 2.17 | 5.69 | 1.98 | 6.75 | 9.31 | 2.13 | 6.22 | 9.47 | 5.60 |
| 16 | Seoul* | Korea | 5.13 | 9.77 | 2.36 | 7.05 | 6.52 | 6.67 | 1.38 | 5.24 | 8.70 | 1.15 | 2.30 | 6.28 | 3.87 | 5.31 | 1.49 | 1.98 | 1.00 | 7.66 | 7.50 | 8.64 | 7.64 | 4.71 | 3.27 | 9.16 | 5.56 |
| 17 | Dubai* | UAE | 7.38 | 5.76 | 5.76 | 7.58 | 9.17 | 8.03 | 1.15 | 2.36 | 2.60 | 1.45 | 1.69 | 1.39 | 4.33 | 3.27 | 2.25 | 1.76 | 3.03 | 7.13 | 2.51 | 9.55 | 9.31 | 2.13 | 6.29 | 9.47 | 5.39 |
| 18 | Hong-Kong* | China | 6.44 | 1.00 | 3.27 | 6.45 | 9.70 | 10.00 | 4.54 | 1.23 | 7.33 | 2.29 | 1.31 | 1.16 | 4.25 | 7.88 | 2.47 | 1.83 | 7.73 | 2.59 | 3.34 | 9.39 | 2.22 | 1.98 | 3.80 | 9.16 | 4.84 |
| 19 | Shenyang | China | 2.78 | 9.85 | 1.91 | 7.66 | 9.02 | 10.00 | 4.54 | 5.39 | 1.61 | 1.83 | 1.31 | 1.00 | 2.44 | 5.31 | 1.30 | 1.68 | 7.18 | 2.66 | 3.57 | 9.24 | 2.22 | 1.98 | 1.08 | 9.16 | 4.46 |
| 20 | KualaLumpur* | Malaysia | 2.78 | 4.55 | 3.80 | 5.84 | 5.54 | 3.57 | 1.69 | 2.21 | 1.00 | 3.72 | 1.76 | 8.76 | 3.57 | 7.13 | 3.42 | 3.19 | 2.25 | 1.45 | 1.61 | 7.13 | 3.06 | 8.79 | 2.97 | 6.57 | 4.42 |
| 21 | Nanjing | China | 8.22 | 9.39 | 1.30 | 7.96 | 8.79 | 10.00 | 4.54 | 5.39 | 1.84 | 1.91 | 1.31 | 1.00 | 2.82 | 5.31 | 1.76 | 1.45 | 8.36 | 3.65 | 5.01 | 9.85 | 2.22 | 1.98 | 1.00 | 3.14 | 4.21 |
| * | WEF | Ranking | |||||||||||||||||||||||||
METHODOLOGY
We researched 100 cities Asia with medium to high positions in the UN Human Development Index. The cities also rank on the China prosperity list and the Asia’s Digital City Index. We aimed to cover a wide range of regions, and prioritised capitals and financial centres.
We analysed the cities for 24 factors that determine a SmartEcoCity, and then ranked the top 20. While the cities at the top of the index deserve praise, those at the bottom should also be given credit as emerging urban spaces making impressive strides towards integrated information networks.
We collected granular level data across a range of criteria: Transport and Mobility, Sustainability, Governance, Innovation Economy, Digitalisation, Cyber Security, Living Standard and Expert Perception. To create the final score, we then ranked the raw data and standardised it out of 10.
Final Score = 22.5% Transport and Mobility(i) + 12.5% Sustainability(i) + 15% Governance(i) + 5% Innovation Economy(i) + 17.5% Digitisation(i) + 10% Living Standard(i) + 7.5% Cyber Security(i) + 10% Expert Perception(i)
Each factor is scored from 1 – 10, the higher the score, the better. Below you can find a description of how each factor was researched.
Cities can be ranked based on their maturity: Asia the most integrated
World Smart City Government Rankings focus Asia
| Rank | City | Score | Vision | Leader | Budget | Financial | Support | Talent | People | Ecosystems | Policies | Track |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | London | 33.5 | 3.1 | 4 | 3 | 4 | 3 | 3.1 | 3 | 4.1 | 3.1 | 3.1 |
| 2 | Singapore | 32.3 | 3 | 4 | 3 | 4.1 | 3 | 3.1 | 2 | 3.1 | 4 | 3 |
| 3 | Seoul | 31.4 | 3.1 | 3 | 3 | 2.2 | 3 | 3 | 4.1 | 3 | 3 | 4 |
| 4 | New York | 31.3 | 3 | 3 | 3 | 3.1 | 3 | 3.1 | 3 | 4 | 2 | 4.1 |
| 5 | Helsinki | 31.2 | 3 | 2 | 4 | 3.1 | 3 | 4 | 3 | 3.1 | 2 | 4 |
| 6 | Montreal | 30.1 | 3.1 | 3 | 3 | 4 | 3 | 2 | 3 | 3 | 3 | 3 |
| 7 | Boston | 29.6 | 3 | 3 | 3 | 2.1 | 3 | 3.1 | 3.1 | 3.1 | 3.1 | 3.1 |
| 8 | Melbourne | 29.5 | 3 | 3 | 3 | 2.1 | 3.1 | 3.1 | 4 | 3.2 | 2 | 3 |
| 9 | Barcelona | 29.4 | 3 | 3 | 3 | 2.1 | 2 | 3.1 | 3 | 3.1 | 3.1 | 4 |
| 10 | Shanghai | 29.2 | 3 | 3 | 4 | 3.1 | 3 | 2 | 2 | 3 | 2.1 | 4 |
| 11 | S. Francisco | 29.1 | 4 | 2 | 3 | 2 | 3 | 3 | 3 | 4 | 3.1 | 2 |
| 12 | Vienna | 28.5 | 4 | 3 | 3 | 2 | 3.1 | 2.1 | 3.1 | 2.1 | 2.1 | 4 |
| 13 | Amsterdam | 28.4 | 3 | 3 | 3 | 4 | 1 | 2.1 | 2.1 | 3.1 | 4 | 3.1 |
| 14 | Shenzhen | 28.3 | 3 | 3 | 4.1 | 3 | 1 | 3 | 2 | 3.1 | 3 | 3.1 |
| 15 | Stockholm | 27.7 | 4.1 | 3 | 3 | 2.1 | 2 | 2.1 | 3.1 | 3.1 | 2.1 | 3.1 |
| 16 | Taipei | 27.6 | 3.1 | 3 | 3 | 2.1 | 2.1 | 2.1 | 3 | 3.1 | 3 | 3.1 |
| 17 | Chicago | 27.4 | 3.1 | 3 | 3 | 2.1 | 1 | 3.1 | 3 | 3 | 2 | 4.1 |
| 18 | Seattle | 27.3 | 4.1 | 2 | 3 | 3 | 2.1 | 3 | 2 | 3.1 | 2 | 3 |
| 19 | Hong Kong | 27.3 | 3.1 | 3 | 3 | 4 | 2.1 | 3 | 2 | 3 | 1.1 | 3 |
| 20 | Charlotte | 27.2 | 3 | 3 | 3 | 2 | 2 | 2.1 | 3 | 3 | 3 | 3.1 |
| 21 | Vancouver | 27.1 | 3 | 3 | 3 | 2 | 2 | 3 | 3.1 | 3 | 2 | 3 |
| 21 | Washington | 27.1 | 4 | 3 | 3 | 2.1 | 2 | 2 | 2 | 3 | 4 | 2 |
| 23 | New Delhi | 27.0 | 3 | 3 | 3 | 2 | 2 | 4 | 3 | 2 | 2 | 3 |
| 24 | Copenhagen | 26.6 | 3 | 3.1 | 3 | 2 | 2 | 2 | 3.2 | 3.2 | 2 | 3.1 |
| 25 | Columbus | 26.4 | 4.1 | 3 | 4 | 3.1 | 3 | 1 | 2.1 | 3.1 | 1 | 2 |
| 26 | Los Angeles | 26.3 | 3 | 3 | 3 | 2 | 2.1 | 3 | 3.1 | 3 | 2.1 | 2 |
| 27 | Surat | 26.2 | 3 | 3 | 3 | 3 | 2 | 2.1 | 3.1 | 2 | 2 | 3 |
| 28 | Tokyo | 26.0 | 4 | 3 | 3 | 2 | 2 | 1 | 2 | 3 | 3 | 3 |
| 29 | Berlin | 25.8 | 3 | 4 | 2 | 2 | 2.1 | 1 | 3.2 | 3.2 | 3.1 | 2.2 |
| 30 | Beijing | 25.5 | 3 | 3 | 3 | 3.2 | 1 | 3.1 | 2 | 3.1 | 2 | 2.1 |
| 31 | Sydney | 25.4 | 3 | 2 | 2 | 2 | 2.1 | 3 | 3.1 | 3.1 | 2.1 | 3 |
| 32 | Ahmedabad | 25.3 | 3 | 3 | 3 | 3.1 | 2 | 2.1 | 2.1 | 3 | 2 | 2 |
| 33 | Bubaneswar | 25.3 | 3 | 3 | 3 | 2 | 2.1 | 2 | 3.1 | 2 | 2 | 3.1 |
| 34 | Jaipur | 25.2 | 3 | 3 | 3 | 2.1 | 2 | 2.1 | 3 | 3 | 2 | 2 |
| 35 | Atlanta | 25.1 | 3 | 2.1 | 3 | 3 | 3 | 2 | 1 | 3 | 2 | 3 |
| 36 | Pune | 25.0 | 3 | 3 | 3 | 2 | 2 | 2 | 4 | 2 | 2 | 2 |
| 37 | Wellington | 24.4 | 3 | 3 | 3 | 1 | 2.1 | 2 | 3 | 2.1 | 2 | 3.2 |
| 38 | Kansas City | 24.3 | 4 | 2 | 4 | 1 | 2.1 | 1 | 3 | 2.1 | 2.1 | 3 |
| 39 | Toronto | 24.2 | 2 | 3 | 3 | 2.1 | 2 | 2 | 2.1 | 3 | 2 | 3 |
| 40 | Dubai | 24.0 | 3 | 3 | 3 | 2 | 2 | 3 | 1 | 2 | 2 | 3 |
| 41 | Dublin | 23.6 | 3 | 4 | 2 | 3 | 2 | 1 | 2.1 | 2.3 | 2.1 | 2.1 |
| 42 | Tel Aviv | 23.3 | 3 | 1 | 3 | 2.1 | 2 | 2.1 | 2 | 4 | 2.1 | 2 |
| 43 | Philadelphia | 23.1 | 2 | 2 | 3 | 2 | 2 | 3 | 2 | 3 | 2.1 | 2 |
| 44 | Reykjavik | 22.8 | 2 | 3 | 2 | 2.1 | 2.1 | 1 | 4.1 | 2.3 | 2 | 2.2 |
| 45 | Lyon | 22.6 | 3 | 3 | 3 | 2.2 | 2 | 2.1 | 2 | 1 | 1 | 3.3 |
| 46 | Paris | 22.4 | 3 | 2 | 3 | 2 | 2 | 2 | 2.1 | 2.1 | 2.2 | 2 |
| 47 | Jakarta | 22.2 | 3 | 3 | 3 | 2 | 2 | 1 | 2 | 2.1 | 2 | 2.1 |
| 48 | Rio Janeiro | 21.2 | 2 | 1 | 2 | 2 | 2 | 2.1 | 3 | 2 | 2.1 | 3 |
| 49 | Phuket | 21.1 | 3 | 2 | 3 | 2.1 | 2 | 2 | 1 | 2 | 2 | 2 |
| 50 | Kigali | 20.0 | 2 | 1 | 2 | 2 | 2 | 3 | 2 | 3 | 1 | 2 |
Ranking Methodology
- VISION. A clear and well-defined strategy to develop a “smart city”
- LEADERSHIP. Dedicated City leadership that steers smart city projects
- BUDGET. Sufficient funding for smart city projects
- FINANCIAL. Financial incentives to effectively encourage private sector participation (e.g. grants, rebates, subsidies, competitions)
- SUPPORT PROGRAMMES. In-kind programmes to encourage private actors to participate (e.g. incubators, events, networks)
- TALENT-READINESS. Programmes to equip the city’s talent with smart skills
- PEOPLE-CENTRICITY. A sincere, people-first design of the future city
- ECOSYSTEMS. A comprehensive range of engaged stakeholders to sustain innovation
- POLICIES. A conducive policy environment for smart city development (e.g. data governance, IP protection, urban design)
- TRACK RECORD. The government’s experience in catalyzing successful smart city initiatives
World SmartEcoCity Index by City
2018 – Smart Cities scored (1-5 High, alphabetical order) according to SECI* with focus US
| City | Country | Smart | Eco | City | SECI |
| Auckland | New Zealand | 2 | 2 | 2 | 6 |
| Bangkok | Thailand | 3 | 1 | 3 | 7 |
| Barcelona | Spain | 4 | 5 | 4 | 9 |
| Berlin | Germany | 3 | 4 | 2 | 9 |
| Bogota | Colombia | 3 | 3 | 0 | 6 |
| Bristol | UK | 2 | 2 | 2 | 6 |
| Cape Town | South Africa | 3 | 1 | 2 | 6 |
| Cleveland | US | 3 | 3 | 4 | 10 |
| Delhi | India | 1 | 1 | 1 | 3 |
| Dubai | UAE | 4 | 3 | 2 | 9 |
| Jeddah | Saudi | 2 | 0 | 0 | 2 |
| Mexico City | Mexico | 3 | 4 | 5 | 12 |
| New York City | US | 5 | 5 | 5 | 15 |
| Paris | France | 4 | 5 | 2 | 11 |
| Pune | India | 2 | 5 | 0 | 7 |
| San Francisco | US | 4 | 5 | 3 | 12 |
| Sao Paulo | Brazil | 1 | 2 | 2 | 5 |
| Shanghai | China | 2 | 3 | 4 | 9 |
| Singapore | Singapore | 4 | 5 | 2 | 11 |
| Tokyo | Japan | 1 | 2 | 2 | 5 |
| Vienna | Austria | 3 | 4 | 2 | 9 |
| Wuxi | China | 2 | 3 | 3 | 8 |
*Index specified according to SEC 3E:
- Smart (Equity): Improving quality of life for citizens, social inclusion, public access, even development for all, making the city more attractive and fair.
- Eco (Environment): Reducing the environmental impact for better liveability, more sustainable city operations and activities, protecting the environment with minimal usage of energy mobility water ressources.
- City (Economy): Involving cityzens in bottom-up co-creation growth initiatives, City-led government (instead of technology companies), clear vision, open innovation, resiliency plan to minimize impact of adverse events: natural disasters, crime, virus pandemic, accidents and pollution.
World SmartEcoCity Index by Country
Coming soon…
China #2, is not number one for AI yet, but plans to take the top spot by 2030. Reasons to believe China will succeed? AI is at the heart of China’s economic strategy. Chinese developers are working on more deep learning projects than their European counterparts, according to the code-collaborating platform GitHub. And the general population is on board: trust in AI is higher in China than anywhere else in the world.
- Greatest strength: Supercomputers. China has almost double the number of super-super-computers than the US. Top 500, non-distributed supercomputers.
- Hotspot: Zhong Guan Cun, also known as Beijing Research Park near Tsinghua University.
- AI expertise: Facial recognition. 85% of patents for facial recognition technology were filed in China, 13 times more than the number of facial recognition patents in the US.