Development of a Quantitative Assessment Framework of Inclusive Green Growth to Improve Policy Decisions


Wolfgang Stenzel (HWK)


Assoc. Prof. Dr. Tania Urmee, Murdoch University (Australia), former HWK Fellow


  • Prof. Dr. Shobhakar Dhakal, Asian Institute of Technology (Thailand)
  • Prof. Dr. Kristine Kern, Leibniz Institute for Research on Society and Space (IRS), Germany, and
    Åbo Akademi University, Finland
  • Dr. Deborah Sunter, University of California Berkeley (USA)
  • Dr. Anis Zaman, Climatec International (Australia), and United Nations Economic and Social Commission for Asia and the Pacific (Thailand)
  • Dr. Cathrin Zengerling LL.M., HafenCity Universität Hamburg


2016 – 2020

Statement of Problem

Decision makers hope to march towards green growth paradigm, looking for appropriate measures of green growth and the key policy drivers that are most effective. Green growth has been largely addressed qualitatively so far and past studies rather present a broader and inconsistent analysis. The domain of green growth studies is dominated by selecting a set of indicators and identification of policies to influence those indicators. In some cases, the alternate scenarios of green growth and the implication of green growth policies have been evaluated for economic, social and environmental domain. The choice of indicators, which is the most important aspect, has been largely subjective and the way through which they affect green growth is poorly attributed. A sound quantitative basis for green growth outcomes from selective set of drivers and their level of influence is urgently needed. Such relations are important because, in doing so, right policies can be identified and devised to influence the most significant drivers for better green growth outcomes. The question is then, what is the most powerful quantitative basis for such policy attribution. Such inference to decision makers must be analytically sound yet simple to understand and interpret. Data Science can be useful in this regards through systematically observing and analyzing the pattern.

Expected Outputs

A long-term output of the Study Group will be a planning tool, to be used by policy makers and development agencies, to measure the progress of green growth in cities. This tool will be published online and be available for free. Several journal articles will also be published at different stages of the project. Application of “data science” to identify the most appropriate green growth indicators will be the innovation that this project will add to the existing body of knowledge.


T. Urmee, Martin Anda, Anna Chapman and Md. Anisuzzaman (2017): Green Growth in cities: two Australian cases; Renewable Energy and Environmental Sustainability, Volume 2, 43 (2017)


July 11 -13, 2016
January 16 - 19, 2017
July 3 - 5, 2017
January 22 - 24, 2018
June 25 - 28, 2018
April 16 - 18, 2019