21
© The Author(s) 2016
R. Clift, A. Druckman (eds.), Taking Stock of Industrial Ecology,
DOI 10.1007/978-3-319-20571-7_2
Chapter 2
Prospective Models of Society’s Future
Metabolism: What Industrial Ecology Has
to Contribute
Stefan Pauliuk and Edgar G. Hertwich
Abstract Scientifi c assessment of sustainable development strategies provides
decision-makers with quantitative information about the strategies’ potential effect.
This assessment is often done by forward-looking or prospective computer models
of society’s metabolism and the natural environment. Computer models in industrial
ecology (IE) have advanced rapidly over the recent years, and now, a new family of
prospective models is available to study the potential effect of sustainable development strategies at full scale.
We outline general principles of prospective modeling and describe the current
development status of two prospective model types: extended dynamic material
fl ow analysis and THEMIS (Technology-Hybridized Environmental-Economic
Model with Integrated Scenarios). These models combine the high level of technological detail known from life-cycle assessment (LCA) and material fl ow analysis
(MFA) with the comprehensiveness of, respectively, dynamic stock models and
input/output analysis (I/O). These models are dynamic; they build future scenarios
with a time horizon until 2050 and beyond. They were applied to study the potential
effect of a wide spectrum of sustainable development strategies, including renewable energy supply, home weatherization, material effi ciency, and light-weighting.
We point out future applications and options for model development and discuss
the relation between prospective IE models and the related concept consequential
LCA (CLCA).
The prospective models for industrial ecology can answer questions that were
previously in the exclusive domain of integrated assessment models (IAMs). A
debate about the relation between the two model families is necessary.
S. Pauliuk (*) • E. G. Hertwich
Industrial Ecology Programme and Department for Energy and Process Engineering ,
Norwegian University of Science and Technology (NTNU) ,
Høgskoleringen 1 , 7491 Trondheim , Norway
e-mail: stefan.pauliuk@ntnu.no
© The Author(s) 2016
R. Clift, A. Druckman (eds.), Taking Stock of Industrial Ecology,
DOI 10.1007/978-3-319-20571-7_2
Chapter 2
Prospective Models of Society’s Future
Metabolism: What Industrial Ecology Has
to Contribute
Stefan Pauliuk and Edgar G. Hertwich
Abstract Scientifi c assessment of sustainable development strategies provides
decision-makers with quantitative information about the strategies’ potential effect.
This assessment is often done by forward-looking or prospective computer models
of society’s metabolism and the natural environment. Computer models in industrial
ecology (IE) have advanced rapidly over the recent years, and now, a new family of
prospective models is available to study the potential effect of sustainable development strategies at full scale.
We outline general principles of prospective modeling and describe the current
development status of two prospective model types: extended dynamic material
fl ow analysis and THEMIS (Technology-Hybridized Environmental-Economic
Model with Integrated Scenarios). These models combine the high level of technological detail known from life-cycle assessment (LCA) and material fl ow analysis
(MFA) with the comprehensiveness of, respectively, dynamic stock models and
input/output analysis (I/O). These models are dynamic; they build future scenarios
with a time horizon until 2050 and beyond. They were applied to study the potential
effect of a wide spectrum of sustainable development strategies, including renewable energy supply, home weatherization, material effi ciency, and light-weighting.
We point out future applications and options for model development and discuss
the relation between prospective IE models and the related concept consequential
LCA (CLCA).
The prospective models for industrial ecology can answer questions that were
previously in the exclusive domain of integrated assessment models (IAMs). A
debate about the relation between the two model families is necessary.
S. Pauliuk (*) • E. G. Hertwich
Industrial Ecology Programme and Department for Energy and Process Engineering ,
Norwegian University of Science and Technology (NTNU) ,
Høgskoleringen 1 , 7491 Trondheim , Norway
e-mail: stefan.pauliuk@ntnu.no