Diffusion of municipal wastewater treatment technologies in China: a collaboration network perspective

Abstract

Real wastewater treatment technology diffusion process was investigated.

The research is based on a dataset of 3136 municipal WWTPs and 4634 organizations.

A new metric was proposed to measure the importance of a project in diffusion.

Important projects usually involve central organizations in collaboration.

Organizations become more central by participating in less important projects.

The diffusion of municipal wastewater treatment technology is vital for urban environment in developing countries. China has built more than 3000 municipal wastewater treatment plants in the past three decades, which is a good chance to understand how technologies diffused in reality. We used a data-driven approach to explore the relationship between the diffusion of wastewater treatment technologies and collaborations between organizations. A database of 3136 municipal wastewater treatment plants and 4634 collaborating organizations was built and transformed into networks for analysis. We have found that: 1) the diffusion networks are assortative, and the patterns of diffusion vary across technologies; while the collaboration networks are fragmented, and have an assortativity around zero since the 2000s. 2) Important projects in technology diffusion usually involve central organizations in collaboration networks, but organizations become more central in collaboration by doing circumstantial projects in diffusion. 3) The importance of projects in diffusion can be predicted with a Random Forest model at a good accuracy and precision level. Our findings provide a quantitative understanding of the technology diffusion processes, which could be used for water-relevant policy-making and business decisions.

Graphical abstract

Keywords

Innovation diffusion / Collaboration network / Wastewater treatment plant / Complex network / Data driven

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Acknowledgements

The research was conducted with financial support from the Tsinghua University Initiative Scientific Research Program (No. 20121088096) and the Major Science and Technology Program for Water Pollution Control and Treatment (Nos. 2012ZX07203-004 and 2012ZX07301-005). We also thank Mr. Tao FU in Peking University for his help in the data collection and analysis.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s11783-017-0903-0 and is accessible for authorized users.

Funding

RIGHTS & PERMISSIONS

2017 Higher Education Press and Springer-Verlag Berlin Heidelberg