102
National Industrial Symbiosis Programme (NISP) in the UK showed that half of all
resources were reused within 32.6 km (Jensen et al. 2011 ). According to the data
from 88 recycling projects in 23 Japanese eco-towns, the average waste collection
and product delivery distance ranged from 15 to 80 km (Chen et al. 2012 ).
Another key question about industrial symbiosis has evolved around the concept
of self-organization and complex adaptive systems. While one stream of IS research
has focused on how to replicate Kalundborg through deliberate planning (Potts Carr
1998 ; Roberts 2004 ; van Leeuwen et al. 2003 ), another stream of studies focused
more on the organic nature of industrial symbiosis development. Based on historical
appraisals, Desrochers ( 2004 ) argued that industrial symbiosis has existed and can
exist primarily through market mechanisms instead of top-down planning. Chertow
( 2007 ) proposed an “uncovering” approach to industrial symbiosis, which stimulates the identifi cation of existing precursors of symbiosis and nurtures them.
Considering industrial symbiosis as a self-organizing phenomenon was then developed further by adopting the framework of complex systems science to understand
industrial ecosystems as complex adaptive systems (Chertow and Ehrenfeld 2012 ).
Along this line of understanding, tools from complex systems began to be applied
to examine evolution and resilience of industrial ecosystems (Cao et al. 2009 ;
Chopra and Khanna 2014 ; Romero and Ruiz 2014 ; Zheng et al. 2013 ; Zhu and Ruth
2013 , 2014 ).
Early studies view industrial symbiosis mainly as a favorable outcome and
focused on exploring ways to implement the most optimal form of industrial symbiosis from technological and economical perspectives. With increasing experiences with successes and failures, however, more studies have examined the role of
social factors (Ashton 2008 ; Gibbs 2003 ; Hewes and Lyons 2008 ; Howard-Grenville
and Paquin 2008 ; Jacobsen 2007 ). Some studies describe industrial symbiosis as a
learning process and link it to innovation for sustainability at local and regional
levels (Mirata and Emtairah 2005 ; Posch 2010 ; Ristola and Mirata 2007 ; Walter and
Scholz 2006 ). Recently, industrial symbiosis was conceptualized as a dynamic process, which can offer new insights about the emergence, evolution, and dissolution
of symbiotic relationships and broader institutional dynamics (Boons et al. 2011 ,
2014 ; Spekkink 2014 ).
Finally, measuring performance of industrial symbiosis has attracted much attention, particularly because economic and environmental benefi ts are what comprise
the core industrial symbiosis approach. Some papers estimated net cost savings for
different industrial symbiosis scenarios (Karlsson and Wolf 2008 ; Martin et al.
1998 ) or for existing industrial symbiosis networks in Kalundborg (Jacobsen 2006 );
Guayama, Puerto Rico (Chertow and Lombardi 2005 ); Oahu, Hawai’i (Chertow and
Miyata 2011 ); and Kawasaki, Japan (Van Berkel et al. 2009a ). Going beyond quantifying cost savings, Wen and Meng ( 2014 ) quantifi ed changes in resource productivity through industrial symbiosis, and Park and Behera ( 2014 ) measured how
symbiosis increases eco-effi ciency. Park and Park ( 2014 ) showed how cost savings
achieved through industrial symbiosis contributed to obtaining competitive advantage in the market.
Understanding the environmental performance of industrial symbiosis began
with quantifying avoided landfi lling or material/energy use reductions. While these
M. Chertow and J. Park
National Industrial Symbiosis Programme (NISP) in the UK showed that half of all
resources were reused within 32.6 km (Jensen et al. 2011 ). According to the data
from 88 recycling projects in 23 Japanese eco-towns, the average waste collection
and product delivery distance ranged from 15 to 80 km (Chen et al. 2012 ).
Another key question about industrial symbiosis has evolved around the concept
of self-organization and complex adaptive systems. While one stream of IS research
has focused on how to replicate Kalundborg through deliberate planning (Potts Carr
1998 ; Roberts 2004 ; van Leeuwen et al. 2003 ), another stream of studies focused
more on the organic nature of industrial symbiosis development. Based on historical
appraisals, Desrochers ( 2004 ) argued that industrial symbiosis has existed and can
exist primarily through market mechanisms instead of top-down planning. Chertow
( 2007 ) proposed an “uncovering” approach to industrial symbiosis, which stimulates the identifi cation of existing precursors of symbiosis and nurtures them.
Considering industrial symbiosis as a self-organizing phenomenon was then developed further by adopting the framework of complex systems science to understand
industrial ecosystems as complex adaptive systems (Chertow and Ehrenfeld 2012 ).
Along this line of understanding, tools from complex systems began to be applied
to examine evolution and resilience of industrial ecosystems (Cao et al. 2009 ;
Chopra and Khanna 2014 ; Romero and Ruiz 2014 ; Zheng et al. 2013 ; Zhu and Ruth
2013 , 2014 ).
Early studies view industrial symbiosis mainly as a favorable outcome and
focused on exploring ways to implement the most optimal form of industrial symbiosis from technological and economical perspectives. With increasing experiences with successes and failures, however, more studies have examined the role of
social factors (Ashton 2008 ; Gibbs 2003 ; Hewes and Lyons 2008 ; Howard-Grenville
and Paquin 2008 ; Jacobsen 2007 ). Some studies describe industrial symbiosis as a
learning process and link it to innovation for sustainability at local and regional
levels (Mirata and Emtairah 2005 ; Posch 2010 ; Ristola and Mirata 2007 ; Walter and
Scholz 2006 ). Recently, industrial symbiosis was conceptualized as a dynamic process, which can offer new insights about the emergence, evolution, and dissolution
of symbiotic relationships and broader institutional dynamics (Boons et al. 2011 ,
2014 ; Spekkink 2014 ).
Finally, measuring performance of industrial symbiosis has attracted much attention, particularly because economic and environmental benefi ts are what comprise
the core industrial symbiosis approach. Some papers estimated net cost savings for
different industrial symbiosis scenarios (Karlsson and Wolf 2008 ; Martin et al.
1998 ) or for existing industrial symbiosis networks in Kalundborg (Jacobsen 2006 );
Guayama, Puerto Rico (Chertow and Lombardi 2005 ); Oahu, Hawai’i (Chertow and
Miyata 2011 ); and Kawasaki, Japan (Van Berkel et al. 2009a ). Going beyond quantifying cost savings, Wen and Meng ( 2014 ) quantifi ed changes in resource productivity through industrial symbiosis, and Park and Behera ( 2014 ) measured how
symbiosis increases eco-effi ciency. Park and Park ( 2014 ) showed how cost savings
achieved through industrial symbiosis contributed to obtaining competitive advantage in the market.
Understanding the environmental performance of industrial symbiosis began
with quantifying avoided landfi lling or material/energy use reductions. While these
M. Chertow and J. Park