30
Fig. 2.1 serves as blueprint for the structure of the system defi nitions of the different
prospective models, including CGEs and IAMs (Pauliuk et al. 2015 ).
3.2.1 Prospective Modeling Using Extended Dynamic MFA
MFA models contain both fl ows and stocks; they are hence a natural starting point
for dynamic and subsequently prospective modeling (Baccini and Bader 1996 ;
Kleijn et al. 2000 ; D. B. Müller et al. 2004 ; van der Voet et al. 2002 ). MFA studies
focus on a few materials or product groups at a time, and it was clear early on that
prospective modeling with such a limited scope requires exogenous assumptions on
the future development of material demand and technological change. In stockdriven modeling, the size of in-use stocks and the lifetime distribution of the different age cohorts are given exogenously, and deconvolution is applied to determine
material demand and scrap supply (D. B. Müller 2006 ). The ability of dynamic
stock models to determine future scrap supply from historic material consumption
has enabled prospective modeling of mass-balanced recycling systems (Busch et al.
2014 ; Hatayama et al. 2009 , 2010 ; Igarashi et al. 2007 ; E. Müller et al. 2014 ;
Murakami et al. 2010 ; Tanikawa et al. 2002 ). These models often distinguish
between different quality levels of secondary material and contain rules for the substitution of secondary for primary metal that are similar to system expansion in
LCA or the by-product technology assumption in I/O (Daigo et al. 2014 ; Hashimoto
et al. 2007 ; Løvik et al. 2014 ; Pauliuk et al. 2012 , 2013a ). Transformation strategies
often affect products and the materials contained therein are not directly addressed.
To understand the role of materials in different transformation strategies, there
hence was a need to include product life cycles into dynamic MFA models, which
led to the development of multilayer MFA and the combination of MFA with process-based LCA and life-cycle impact assessment (Milford et al. 2013 ; Pauliuk
et al. 2013b ; Sandberg and Brattebø 2012 ; Pauliuk 2013 ).
State-of-the-art extended dynamic MFA models comprise these different trends
and provide large-scale and long-term dynamic assessments of specifi c transformation strategies, such as material effi ciency (Milford et al. 2013 ) or passenger vehicle
light-weighting (Modaresi et al. 2014 ). Starting from scenario assumptions on stock
size and technology choice, these models apply stock-driven modeling to determine
the levels of material production and energy supply that are required to build, operate, and dispose of the product stocks. They contain material-balanced process
models of the industrial system and use satellite accounts to track resource consumption, energy supply, and emissions to the environment. A special feature of
dynamic extended MFA is the high level of detail of the material cycles in the system, the distinction between open-loop recycling (“downcycling”) and proper recycling, and their capability to quantify how changes in material production and
recycling systems impact the overall effect of a certain transformation strategy.
S. Pauliuk and E.G. Hertwich
Fig. 2.1 serves as blueprint for the structure of the system defi nitions of the different
prospective models, including CGEs and IAMs (Pauliuk et al. 2015 ).
3.2.1 Prospective Modeling Using Extended Dynamic MFA
MFA models contain both fl ows and stocks; they are hence a natural starting point
for dynamic and subsequently prospective modeling (Baccini and Bader 1996 ;
Kleijn et al. 2000 ; D. B. Müller et al. 2004 ; van der Voet et al. 2002 ). MFA studies
focus on a few materials or product groups at a time, and it was clear early on that
prospective modeling with such a limited scope requires exogenous assumptions on
the future development of material demand and technological change. In stockdriven modeling, the size of in-use stocks and the lifetime distribution of the different age cohorts are given exogenously, and deconvolution is applied to determine
material demand and scrap supply (D. B. Müller 2006 ). The ability of dynamic
stock models to determine future scrap supply from historic material consumption
has enabled prospective modeling of mass-balanced recycling systems (Busch et al.
2014 ; Hatayama et al. 2009 , 2010 ; Igarashi et al. 2007 ; E. Müller et al. 2014 ;
Murakami et al. 2010 ; Tanikawa et al. 2002 ). These models often distinguish
between different quality levels of secondary material and contain rules for the substitution of secondary for primary metal that are similar to system expansion in
LCA or the by-product technology assumption in I/O (Daigo et al. 2014 ; Hashimoto
et al. 2007 ; Løvik et al. 2014 ; Pauliuk et al. 2012 , 2013a ). Transformation strategies
often affect products and the materials contained therein are not directly addressed.
To understand the role of materials in different transformation strategies, there
hence was a need to include product life cycles into dynamic MFA models, which
led to the development of multilayer MFA and the combination of MFA with process-based LCA and life-cycle impact assessment (Milford et al. 2013 ; Pauliuk
et al. 2013b ; Sandberg and Brattebø 2012 ; Pauliuk 2013 ).
State-of-the-art extended dynamic MFA models comprise these different trends
and provide large-scale and long-term dynamic assessments of specifi c transformation strategies, such as material effi ciency (Milford et al. 2013 ) or passenger vehicle
light-weighting (Modaresi et al. 2014 ). Starting from scenario assumptions on stock
size and technology choice, these models apply stock-driven modeling to determine
the levels of material production and energy supply that are required to build, operate, and dispose of the product stocks. They contain material-balanced process
models of the industrial system and use satellite accounts to track resource consumption, energy supply, and emissions to the environment. A special feature of
dynamic extended MFA is the high level of detail of the material cycles in the system, the distinction between open-loop recycling (“downcycling”) and proper recycling, and their capability to quantify how changes in material production and
recycling systems impact the overall effect of a certain transformation strategy.
S. Pauliuk and E.G. Hertwich