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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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Front. Environ. Sci. Eng.    2017, Vol. 11 Issue (1) : 11    https://doi.org/10.1007/s11783-017-0903-0
RESEARCH ARTICLE
Diffusion of municipal wastewater treatment technologies in China: a collaboration network perspective
Yang Li1,Lei Shi1(),Yi Qian1,Jie Tang2
1. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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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.
Keywords Innovation diffusion      Collaboration network      Wastewater treatment plant      Complex network      Data driven     
PACS:     
Fund: 
Corresponding Author(s): Lei Shi   
Issue Date: 22 January 2017
 Cite this article:   
Yang Li,Lei Shi,Yi Qian, et al. Diffusion of municipal wastewater treatment technologies in China: a collaboration network perspective[J]. Front. Environ. Sci. Eng., 2017, 11(1): 11.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-017-0903-0
https://academic.hep.com.cn/fese/EN/Y2017/V11/I1/11
Fig.1  Visualization of the aggregated (a) diffusion network (layered layout by the built year) and (b) collaboration network (force directed layout)
year new projects nodes edges proportion of largest connected component assortativity
1981 1 1 0 1.000 insufficient data
1982 0 1 0 1.000 insufficient data
1983 2 3 0 0.333 insufficient data
1984 3 6 0 0.167 insufficient data
1985 1 7 0 0.143 insufficient data
1986 6 13 0 0.077 insufficient data
1987 2 15 0 0.067 insufficient data
1988 3 18 1 0.111 insufficient data
1989 5 23 1 0.087 insufficient data
1990 4 27 1 0.074 insufficient data
1991 5 32 3 0.094 -0.500
1992 5 37 3 0.081 -0.500
1993 8 45 3 0.067 -0.500
1994 7 52 5 0.058 0.167
1995 7 59 5 0.051 0.167
1996 4 63 5 0.048 0.167
1997 10 73 9 0.041 0.060
1998 18 91 12 0.033 0.158
1999 23 114 20 0.035 -0.275
2000 28 142 35 0.049 0.506
2001 62 204 92 0.078 0.740
2002 89 293 167 0.078 0.447
2003 133 426 319 0.089 0.545
2004 155 581 612 0.093 0.424
2005 144 725 841 0.103 0.496
2006 252 977 1202 0.089 0.521
2007 359 1336 2101 0.093 0.602
2008 356 1692 3134 0.112 0.581
2009 685 2377 4866 0.126 0.517
2010 661 3038 7989 0.138 0.426
2011 98 3136 8712 0.136 0.401
Tab.1  Properties of diffusion networks in 1981–2011
Fig.2  Visualization of the diffusion networks of five treatment technologies (A2O= anaerobic-anoxic-oxic, A/O= anaerobic-oxic, AS= traditional activated sludge, OD= oxidation ditch, SBR= sequencing batch reactor)
Fig.3  Evolution of relative size for the two largest components in the network
Fig.4  Assortativity of collaboration networks
Fig.5  Kernel density plot of project centrality distributions in Ptop and Pordinary: (a) kernel density plot of degree; (b) kernel density plot of betweenness; (c) kernel density plot of k-core
Fig.6  Difference of an organization’s centrality against the max importance value of its projects: (a) the difference of degree; (b) the difference of betweenness; (c) the difference of k-core
year of projects in training set number of projects in training set year of projects in testing set number of projects in testing set AUC
1981–2000 142 2001 62 0.733
1981–2001 204 2002 89 0.837
1981–2002 293 2003 133 0.741
1981–2003 426 2004 155 0.639
1981–2004 581 2005 144 0.692
1981–2005 725 2006 252 0.660
Tab.2  Training and testing set and the performance of Random Forest models
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