41
Kleijn, R., Huele, R., & van der Voet, E. (2000). Dynamic substance fl ow analysis: The delaying
mechanism of stocks, with the case of PVC in Sweden. Ecological Economics, 32 (2),
241–254.
Krausmann, F. (2011). The socio-metabolic transition. Long term historical trends and patterns in
global material and energy use . Vienna: Institute of Social Ecology.
Krausmann, F., & Fischer-Kowalski, M. (2013). Global socio-metabolic transitions. In S. J. Singh,
H. Haberl, M. Chertow, M. Mirtl, & M. Schmid (Eds.), Long term socio-ecological research
(Human-envi, pp. 339–365). Dordrecht: Springer.
Leontief, W. W., & Duchin, F. (1986). The future impact of automation on workers . New York:
Oxford University Press.
Levine, S. H., Gloria, T. P., & Romanoff, E. (2007). A dynamic model for determining the temporal distribution of environmental burden. Journal of Industrial Ecology, 11 (4), 39–49.
Loulou, R., Remne, U., Kanudia, A., Lehtila, A., & Goldstein, G. (2005). Documentation for the
TIMES model (pp. 1–78). Paris: Energy Technology Systems Analysis Programme (ETSAP).
Løvik, A. N., Modaresi, R., & Müller, D. B. (2014). Long-term strategies for increased recycling
of automotive aluminum and its alloying elements. Environmental Science & Technology,
48 (8), 4257–4265.
Lundie, S., Peters, G. M., & Beavis, P. C. (2004). Life cycle assessment for sustainable metropolitan water systems planning. Environmental Science & Technology , 38 (13), 3465–3473.
Majeau-Bettez, G., Wood, R., & Strømman, A. H. (2014). Unifi ed theory of allocations and constructs in life cycle assessment and input-output analysis. Journal of Industrial Ecology, 18 (5),
747–770.
Milford, R. L., Pauliuk, S., Allwood, J. M., & Müller, D. B. (2013). The roles of energy and material effi ciency in meeting steel industry CO2 targets. Environmental Science & Technology,
47 (7), 3455–3462.
Modaresi, R., & Müller, D. B. (2012). The role of automobiles for the future of aluminum recycling. Environmental Science & Technology, 46 (16), 8587–8594.
Modaresi, R., Pauliuk, S., Løvik, A. N., & Müller, D. B. (2014). Global carbon benefi ts of material
substitution in passenger cars until 2050 and the impact on the steel and aluminum industries.
Environmental Science & Technology, 48 (18), 10776–10784.
Müller, D. B. (2006). Stock dynamics for forecasting material fl ows – Case study for housing in
The Netherlands. Ecological Economics, 59 (1), 142–156.
Müller, D. B., Bader, H.-P., & Baccini, P. (2004). Long-term coordination of timber production and
consumption using a dynamic material and energy fl ow analysis. Journal of Industrial Ecology,
8 (3), 65–87.
Müller, E., Hilty, L. M., Widmer, R., Schluep, M., & Faulstich, M. (2014). Modeling metal stocks
and fl ows – A review of dynamic material fl ow analysis methods. Environmental Science &
Technology, 48 (4), 2102–2113.
Murakami, S., Oguchi, M., Tasaki, T., Daigo, I., & Hashimoto, S. (2010). Lifespan of commodities, Part I – The creation of a database and its review. Journal of Industrial Ecology, 14 (4),
598–612.
Nakamura, S., Nakajima, K., Kondo, Y., & Nagasaka, T. (2007). The waste input-output approach
to materials fl ow analysis concepts and application to base metals. Journal of Industrial
Ecology, 11 (4), 50–63.
Nakamura, S., Kondo, Y., Kagawa, S., Matsubae, K., Nakajima, K., & Nagasaka, T. (2014).
MaTrace: Tracing the fate of materials over time and across products in open-loop recycling.
Environmental Science & Technology, 48 (13), 7207–7214.
Northey, S., Mohr, S., Mudd, G. M., Weng, Z., & Giurco, D. (2014). Modelling future copper ore
grade decline based on a detailed assessment of copper resources and mining. Resources,
Conservation and Recycling, 83 , 190–201.
OECD/IEA. (2010). Energy technology perspectives : Scenarios and strategies to 2050 . Paris:
International Energy Agency.
2 Prospective Models of Society’s Future Metabolism: What Industrial Ecology Has…
Kleijn, R., Huele, R., & van der Voet, E. (2000). Dynamic substance fl ow analysis: The delaying
mechanism of stocks, with the case of PVC in Sweden. Ecological Economics, 32 (2),
241–254.
Krausmann, F. (2011). The socio-metabolic transition. Long term historical trends and patterns in
global material and energy use . Vienna: Institute of Social Ecology.
Krausmann, F., & Fischer-Kowalski, M. (2013). Global socio-metabolic transitions. In S. J. Singh,
H. Haberl, M. Chertow, M. Mirtl, & M. Schmid (Eds.), Long term socio-ecological research
(Human-envi, pp. 339–365). Dordrecht: Springer.
Leontief, W. W., & Duchin, F. (1986). The future impact of automation on workers . New York:
Oxford University Press.
Levine, S. H., Gloria, T. P., & Romanoff, E. (2007). A dynamic model for determining the temporal distribution of environmental burden. Journal of Industrial Ecology, 11 (4), 39–49.
Loulou, R., Remne, U., Kanudia, A., Lehtila, A., & Goldstein, G. (2005). Documentation for the
TIMES model (pp. 1–78). Paris: Energy Technology Systems Analysis Programme (ETSAP).
Løvik, A. N., Modaresi, R., & Müller, D. B. (2014). Long-term strategies for increased recycling
of automotive aluminum and its alloying elements. Environmental Science & Technology,
48 (8), 4257–4265.
Lundie, S., Peters, G. M., & Beavis, P. C. (2004). Life cycle assessment for sustainable metropolitan water systems planning. Environmental Science & Technology , 38 (13), 3465–3473.
Majeau-Bettez, G., Wood, R., & Strømman, A. H. (2014). Unifi ed theory of allocations and constructs in life cycle assessment and input-output analysis. Journal of Industrial Ecology, 18 (5),
747–770.
Milford, R. L., Pauliuk, S., Allwood, J. M., & Müller, D. B. (2013). The roles of energy and material effi ciency in meeting steel industry CO2 targets. Environmental Science & Technology,
47 (7), 3455–3462.
Modaresi, R., & Müller, D. B. (2012). The role of automobiles for the future of aluminum recycling. Environmental Science & Technology, 46 (16), 8587–8594.
Modaresi, R., Pauliuk, S., Løvik, A. N., & Müller, D. B. (2014). Global carbon benefi ts of material
substitution in passenger cars until 2050 and the impact on the steel and aluminum industries.
Environmental Science & Technology, 48 (18), 10776–10784.
Müller, D. B. (2006). Stock dynamics for forecasting material fl ows – Case study for housing in
The Netherlands. Ecological Economics, 59 (1), 142–156.
Müller, D. B., Bader, H.-P., & Baccini, P. (2004). Long-term coordination of timber production and
consumption using a dynamic material and energy fl ow analysis. Journal of Industrial Ecology,
8 (3), 65–87.
Müller, E., Hilty, L. M., Widmer, R., Schluep, M., & Faulstich, M. (2014). Modeling metal stocks
and fl ows – A review of dynamic material fl ow analysis methods. Environmental Science &
Technology, 48 (4), 2102–2113.
Murakami, S., Oguchi, M., Tasaki, T., Daigo, I., & Hashimoto, S. (2010). Lifespan of commodities, Part I – The creation of a database and its review. Journal of Industrial Ecology, 14 (4),
598–612.
Nakamura, S., Nakajima, K., Kondo, Y., & Nagasaka, T. (2007). The waste input-output approach
to materials fl ow analysis concepts and application to base metals. Journal of Industrial
Ecology, 11 (4), 50–63.
Nakamura, S., Kondo, Y., Kagawa, S., Matsubae, K., Nakajima, K., & Nagasaka, T. (2014).
MaTrace: Tracing the fate of materials over time and across products in open-loop recycling.
Environmental Science & Technology, 48 (13), 7207–7214.
Northey, S., Mohr, S., Mudd, G. M., Weng, Z., & Giurco, D. (2014). Modelling future copper ore
grade decline based on a detailed assessment of copper resources and mining. Resources,
Conservation and Recycling, 83 , 190–201.
OECD/IEA. (2010). Energy technology perspectives : Scenarios and strategies to 2050 . Paris:
International Energy Agency.
2 Prospective Models of Society’s Future Metabolism: What Industrial Ecology Has…