The Global Startup Ecosystem Report: Cleantech Edition

Methodology & References


The Startup Genome quantitative data infrastructure includes data on over 3 million companies, 280+ ecosystems, and survey data from more than 10,000 startup executives across the globe.

These are the main datasets that make up this data science infrastructure:

  • Startup Genome proprietary data:
    • Interviews with 100+ experts
    • 2017–2021 Startup Ecosystem Survey with more than 10,000 participants per year
  • Crunchbase: global dataset on funding, exits, and locations of startups and investors
  • Dealroom: global dataset on funding, exits, and locations of startups and investors
  • Pitchbook: private capital market data provider
  • Local partners (accelerators, incubators, startup hubs, investors):
    • list of startups
    • list of local exits and funding events
  • CB Insights: global dataset on unicorns

Sub-Sector Definition

Cleantech consists of sustainable solutions in the fields of Energy, Water, Transportation, Agriculture, and Manufacturing that include advanced materials, smart grids, water treatment, efficient energy storage, and distributed energy systems.

Data Sources

Primary Data Sources

  • Startup Genome LLC (2017-2021). Database
  • BV. (2017-2021). Database
  • Crunchbase (2017-2021). Database
  • CB Insights (2019-2021). Database
  • Pitchbook (2018-2021), a private capital market data provider Database

Secondary Data Sources

  • Forbes 2000
  • International IP Index
  • Shanghai Rankings
  • WIPO

Cleantech Sub-Sectors Ranking Methodology (For Top Ecosystems)

Overall Ranking

The overall global ecosystem ranking is a weighted average of the following factor scores:

  • Performance: 25%
  • Funding: 20%
  • Startup Experience: 15%
  • Knowledge: 10%
  • Talent: 5%
  • Focus: 25%

The success factors are weighted by the above percentages to establish the overall rank of each ecosystem.

The weights of the factors were determined from research and experience. This method takes into account what is deemed to be of importance in ecosystem mapping and analysis overall and across all ecosystem sizes. We have used our research in the period of 2009-2020 and correlation analysis and modeling work based on linear regression analyses, using factor indexes as independent variables with the performance index as dependent variable.

Adding the actual Performance Index to the ranking formula serves to include the influence of unobserved factors on the performance of an ecosystem.

Ranking Details


Captures the actual leading, current, and lagging indicators of ecosystem performance.

  • 50% number of exits of $50M+ in time period of July 1, 2016 to June 30, 2021
  • 25% number of exits all in time period of July 1, 2016 to June 30, 2021
  • 25% log of Output (number of startups in Cleantech) in the time period of July 01, 2016 to June 30, 2021.


Quantifies funding metrics important to the success of early-stage startups.

  • 90% log of number of early-stage funding deals in the time period of July 1, 2016 to June 30, 2021
  • 10% log of number of Series B funding deals in the time period of July 1, 2016 to June 30, 2021.


  • 50% Tech Potential, a measure calculated at the technology class level globally and calculated for each ecosystem based on the technologies it produces
    • 20% Complexity of Technology Class, based on a PageRank algorithm
    • 30% Global Growth of Technology Class
    • 50% Size of Technology Class (log of number of global patents in class)
  • 40% Ecosystem Knowledge Space Complexity, a measure of the capacity of the ecosystem for producing patent in complex technology classes, based on a PageRank algorithm3
  • 10% Patents (log of number of patents related to the sub-sector)  


Assesses the talent Cleantech startups have access to. For this Factor, we analyzed all subjects included in Shanghai Rankings and matched to the relevant Startup Sub-Sectors to calculate the following metrics:

  • 30% Average of CNCI score from Shanghai Rankings
  • 40% Average of TOP score from Shanghai Rankings
  • 30% Average Q1 Score from Shanghai Rankings


  • 90% Venture A Funding (log of number of historical Series A funding deals, as a proxy for number of teams that raised funds in the ecosystem in the time period of July 01, 2011 to June 30, 2021.
  • 10% Exits (log of number of historical $50M+ exits, as a proxy for number of scaled teams in the ecosystem in the time period of July 01, 2011 to June 30, 2021.


  • 50% Number of Employees (log of number of employees in Forbes 2000 companies related to the sub-sector)
  • 50% Market Cap (log of sum of market value of Forbes 2000 companies related to the sub-sector)


  •  % share of startups in the sub-sector.

Key Concepts and Definitions

Ranking Score

The ranking is primarily driven by one question: In which ecosystems can early-stage startups have the best chance of building global success?


A technology-enabled business that is less than 10 years old. Steve Blank defines a startup as a “temporary organization in search of a repeatable and scalable business Model. We use this definition to look across sectors and sub-sectors, including software, hardware, health, energy, and others, and we use these parameters for data collection purposes.

Startup Ecosystem

A shared pool of resources, generally located within a 60-mile (100-kilometer) radius around a center point in a given region, with a few exceptions based on local realities. Resources typically include  policymakers, accelerators, incubators, coworking spaces, educational institutions and funding groups.

Ecosystem Success Factors Model

Our principal analytical tool, this measures different dimensions that support the performance of local startups. We look at multiple factors for our rankings: one measuring actual performance, with other  Success Factors associated with performance, each comprising sub-factors and metrics. These factors are highlighted in our Ranking Methodology section, as well as in each rankings section.

  • Performance: A combination of leading, lagging, and current indicators that
  • capture economic outcomes in a startup ecosystem.
  • Funding: The level and growth of early-stage funding, looking at both access and quality.
  • Startup Experience: The depth and diversity of the pool of prior startup experience in an ecosystem.
  • Talent: Measures the accessibility, quality, and cost of software engineering expertise.


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