Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Industrial ecology in integrated assessment models

Abstract

Technology-rich integrated assessment models (IAMs) address possible technology mixes and future costs of climate change mitigation by generating scenarios for the future industrial system. Industrial ecology (IE) focuses on the empirical analysis of this system. We conduct an in-depth review of five major IAMs from an IE perspective and reveal differences between the two fields regarding the modelling of linkages in the industrial system, focussing on AIM/CGE, GCAM, IMAGE, MESSAGE, and REMIND. IAMs ignore material cycles and recycling, incoherently describe the life-cycle impacts of technology, and miss linkages regarding buildings and infrastructure. Adding IE system linkages to IAMs adds new constraints and allows for studying new mitigation options, both of which may lead to more robust and policy-relevant mitigation scenarios.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Schematic of the general structure of integrated assessment models (IAMs).
Figure 2: Overview of the main descriptive and assessment methods in industrial ecology.
Figure 3: Visualization of the integration of the IAM and IE perspectives on society's biophysical basis.

References

  1. IPCC Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) (Cambridge Univ. Press, 2015).

  2. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    CAS  Google Scholar 

  3. McJeon, H. C. et al. Limited impact on decadal-scale climate change from increased use of natural gas. Nature 514, 482–485 (2014).

    CAS  Google Scholar 

  4. Glynn, J. et al. Informing energy and climate policies using energy systems models. 30, 359–387 (2015).

  5. Grubler, A. et al. The Global Energy Assessment: Toward a More Sustainable Future (GEA, IIASA, 2012).

    Google Scholar 

  6. Hejazi, M. et al. Long-term global water projections using six socioeconomic scenarios in an integrated assessment modeling framework. Technol. Forecast. Soc. Change 81, 205–226 (2014).

    Google Scholar 

  7. Energy Technology Perspectives 2015 (IEA, 2015).

  8. Smith, S. J. et al. Long history of IAM comparisons. Nat. Clim. Change 5, 391 (2015).

    Google Scholar 

  9. Kriegler, E. et al. Making or breaking climate targets: the AMPERE study on staged accession scenarios for climate policy. Technol. Forecast. Soc. Change 90, 24–44 (2015).

    Google Scholar 

  10. Fischer-Kowalski, M. & Weisz, H. Society as hybrid between material and symbolic realms: toward a theoretical framework of society-nature interaction. Adv. Hum. Ecol. 8, 215–251 (1999).

    Google Scholar 

  11. Hellweg, S. & Milà i Canals, L. Emerging approaches, challenges and opportunities in life cycle assessment. Science 344, 1109–1113 (2014).

    CAS  Google Scholar 

  12. Wiedmann, T. O. et al. The material footprint of nations. Proc. Natl Acad. Sci. USA 112, 6271–6276 (2015).

    CAS  Google Scholar 

  13. Pauliuk, S. & Müller, D. B. The role of in-use stocks in the social metabolism and in climate change mitigation. Glob. Environ. Change 24, 132–142 (2014).

    Google Scholar 

  14. Müller, D. B., Wang, T., Duval, B. & Graedel, T. E. Exploring the engine of anthropogenic iron cycles. Proc. Natl Acad. Sci. USA 103, 16111–16116 (2006).

    Google Scholar 

  15. Graedel, T. E., Harper, E. M., Nassar, N. T. & Reck, B. K. On the materials basis of modern society. Proc. Natl Acad. Sci. USA 112, 6295–6300 (2013).

    Google Scholar 

  16. Lenzen, M. & Reynolds, C. J. A supply-use approach to waste input-output analysis. J. Ind. Ecol. 18, 212–226 (2014).

    Google Scholar 

  17. Chertow, M. R. 'Uncovering' industrial symbiosis. J. Ind. Ecol. 11, 11–30 (2007).

    Google Scholar 

  18. Kennedy, C. A. et al. Energy and material flows of megacities. Proc. Natl Acad. Sci. USA 112, 5985–5990 (2015).

    CAS  Google Scholar 

  19. Ramaswami, A., Chavez, A. & Chertow, M. Carbon footprinting of cities and implications for analysis of urban material and energy flows. J. Ind. Ecol. 16, 783–785 (2012).

    Google Scholar 

  20. Creutzig, F. et al. Reconciling top-down and bottom-up modelling on future bioenergy deployment. Nat. Clim. Change 2, 320–327 (2012).

    Google Scholar 

  21. Dandres, T., Gaudreault, C., Tirado-Seco, P. & Samson, R. Assessing non-marginal variations with consequential LCA: application to European energy sector. Renew. Sustain. Energy Rev. 15, 3121–3132 (2011).

    Google Scholar 

  22. Earles, J. M. & Halog, A. Consequential life cycle assessment: a review. Int. J. Life Cycle Assess. 16, 445–453 (2011).

    Google Scholar 

  23. Daly, H. E., Scott, K., Strachan, N. & Barrett, J. R. The indirect CO2 emission implications of energy system pathways: Linking IO and TIMES models for the UK. Environ. Sci. Technol. 49, 10701–10709 (2015).

    CAS  Google Scholar 

  24. Pauliuk, S. & Hertwich, E. G. in Taking Stock of Industrial Ecology (eds Clift, R. & Duckmann, A.) 21–43 (Springer, 2016).

    Google Scholar 

  25. Hackett, S. B. & Moxnes, E. Natural capital in integrated assessment models of climate change. Ecol. Econ. 116, 354–361 (2015).

    Google Scholar 

  26. Harfoot, M. et al. Integrated assessment models for ecologists: the present and the future. Glob. Ecol. Biogeogr. 23, 124–143 (2014).

    Google Scholar 

  27. Strachan, N., Fais, B. & Daly, H. E. Reinventing the energy modelling–policy interface. Nat. Energy 1, 16012 (2016).

    Google Scholar 

  28. Peters, G. P. The 'best available science' to inform 1.5 °C policy choices. Nat. Clim. Change 6, 646–649 (2016).

    Google Scholar 

  29. Stern, N. Current climate models are grossly misleading. Nature 530, 407–409 (2016).

    Google Scholar 

  30. Hertwich, E. G. & Peters, G. P. Carbon footprint of nations: a global, trade-linked analysis. Environ. Sci. Technol. 43, 6414–6420 (2009).

    CAS  Google Scholar 

  31. Wiedmann, T. O. et al. A carbon footprint time series of the UK – results from a multi-region input–output model. Econ. Syst. Res. 22, 19–42 (2010).

    Google Scholar 

  32. Weinzettel, J., Hertwich, E. G., Peters, G. P., Steen-Olsen, K. & Galli, A. Affluence drives the global displacement of land use. Glob. Environ. Change 23, 433–438 (2013).

    Google Scholar 

  33. Lenzen, M. et al. International trade drives biodiversity threats in developing nations. Nature 486, 109–112 (2012).

    CAS  Google Scholar 

  34. Hertwich, E. G. et al. Integrated life-cycle assessment of electricity-supply scenarios confirms global environmental benefit of low-carbon technologies. Proc. Natl Acad. Sci. USA 112, 6277–6282 (2015).

    CAS  Google Scholar 

  35. Hawkins, T. R., Singh, B., Majeau-Bettez, G. & Strømman, A. H. Comparative environmental life cycle assessment of conventional and electric vehicles. J. Ind. Ecol. 17, 53–64 (2013).

    CAS  Google Scholar 

  36. Frischknecht, R. et al. The environmental relevance of capital goods in life cycle assessments of products and services. Int. J. Life Cycle Assess. 12, 7–17 (2007).

    CAS  Google Scholar 

  37. Yeh, S. & Sperling, D. Low carbon fuel standards: Implementation scenarios and challenges. Energy Policy 38, 6955–6965 (2010).

    Google Scholar 

  38. Creutzig, F., McGlynn, E., Minx, J. & Edenhofer, O. Climate policies for road transport revisited (I): evaluation of the current framework. Energy Policy 39, 2396–2406 (2011).

    CAS  Google Scholar 

  39. Grübler, A. The Rise and Fall of Infrastructures. (Physica-Verlag Heidelberg, 1990).

    Google Scholar 

  40. Müller, D. B. Stock dynamics for forecasting material flows - case study for housing in the Netherlands. Ecol. Econ. 59, 142–156 (2006).

    Google Scholar 

  41. Elshkaki, A., Graedel, T. E., Ciacci, L. & Reck, B. K. Copper demand, supply, and associated energy use to 2050. Glob. Environ. Change 39, 305–315 (2016).

    Google Scholar 

  42. Tanikawa, H. & Hashimoto, S. Urban stock over time: spatial material stock analysis using 4d-GIS. Build. Res. Inf. 37, 483–502 (2009).

    Google Scholar 

  43. Fischedick, M. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 10 (IPCC, Cambridge Univ. Press, 2015).

    Google Scholar 

  44. Liu, G., Bangs, C. E. & Müller, D. B. Stock dynamics and emission pathways of the global aluminium cycle. Nat. Clim. Change 2, 338–342 (2012).

    Google Scholar 

  45. Milford, R. L., Pauliuk, S., Allwood, J. M. & Müller, D. B. The roles of energy and material efficiency in meeting steel industry CO2 targets. Environ. Sci. Technol. 47, 3455–3462 (2013).

    CAS  Google Scholar 

  46. Allwood, J. M., Cullen, J. M. & Milford, R. L. Options for achieving a 50% cut in industrial carbon emissions by 2050. Environ. Sci. Technol. 44, 1888–1894 (2010).

    CAS  Google Scholar 

  47. Ayres, R. U. & Kneese, A. Production, consumption, and externalities. Amer. Econ. Rev. 59, 282–297 (1969).

    Google Scholar 

  48. Frischknecht, R. et al. The ecoinvent database: overview and methodological framework. Int. J. Life Cycle Assess. 10, 3–9 (2005).

    CAS  Google Scholar 

  49. Finnveden, G. et al. Recent developments in life cycle assessment. J. Environ. Manage. 91, 1–21 (2009).

    Google Scholar 

  50. Graedel, T. E. et al. Multilevel cycle of anthropogenic copper. Environ. Sci. Technol. 38, 1242–1252 (2004).

    CAS  Google Scholar 

  51. Nakamura, S. & Kondo, Y. Input-output analysis of waste management. J. Ind. Ecol. 6, 39–63 (2002).

    Google Scholar 

  52. Majeau-Bettez, G., Wood, R. & Strømman, A. H. Unified theory of allocations and constructs in life cycle assessment and input-output analysis. J. Ind. Ecol. 18, 747–770 (2014).

    CAS  Google Scholar 

  53. Yu, C., Davis, C. & Dijkema, G. P. J. Understanding the evolution of industrial symbiosis research. J. Ind. Ecol. 18, 280–293 (2014).

    Google Scholar 

  54. Kennedy, C. A., Cuddihy, J. & Engel-Yan, J. The changing metabolism of cities. J. Ind. Ecol. 11, 43–59 (2007).

    CAS  Google Scholar 

  55. Kenworthy, J. R. & Laube, F. B. Patterns of automobile dependency in cities: an international overview of key physical and economic dimensions with some applications for urban policy. Transp. Res. Part A 33, 691–723 (1999).

    Google Scholar 

  56. Seto, K. C. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2015).

    Google Scholar 

  57. Keirstead, J., Jennings, M. & Sivakumar, A. A review of urban energy system models: approaches, challenges and opportunities. Renew. Sustain. Energy Rev. 16, 3847–3866 (2012).

    Google Scholar 

  58. Rosen, R. A. IAMs and peer review. Nat. Clim. Change 5, 390 (2015).

    Google Scholar 

  59. Rosen, R. A. Critical review of: “Making or breaking climate targets — the AMPERE study on staged accession scenarios for climate policy”. Technol. Forecast. Soc. Change 96, 322–326 (2015).

    Google Scholar 

  60. Nakata, T. Energy-economic models and the environment. Prog. Energy Combust. Sci. 30, 417–475 (2004).

    Google Scholar 

  61. Fleiter, T., Worrell, E. & Eichhammer, W. Barriers to energy efficiency in industrial bottom-up energy demand models — a review. Renew. Sustain. Energy Rev. 15, 3099–3111 (2011).

    Google Scholar 

  62. Mundaca, L., Neij, L., Worrell, E. & McNeil, M. Evaluating energy efficiency policies with energy-economy models. Annu. Rev. Environ. Resour. 35, 305–344 (2010).

    Google Scholar 

  63. Worrell, E., Ramesohl, S. & Boyd, G. Advances in energy forecasting models based on engineering economics. Annu. Rev. Environ. Resour. 29, 345–381 (2004).

    Google Scholar 

  64. Nakata, T., Silva, D. & Rodionov, M. Application of energy system models for designing a low-carbon society. Prog. Energy Combust. Sci. 37, 462–502 (2011).

    CAS  Google Scholar 

  65. Grubb, M., Köhler, J. & Anderson, D. Induced technical change in energy and environmental modelling: analytic approaches and policy implications. Annu. Rev. Energy Environ. 27, 271–308 (2002).

    Google Scholar 

  66. Weyant, J. P. & Olavson, T. Issues in modeling induced technological change in energy, environmental, and climate policy. Environ. Model. Assess. 4, 67–85 (1999).

    Google Scholar 

  67. Matsuoka, Y., Morita, T. & Kainuma, M. in Present and Future of Modeling Global Environmental Change: Toward Integrated Modeling (eds Matsuno, T. & Kida, H.) 339–361 (TERRAPUB, 2001).

    Google Scholar 

  68. Dai, H., Masui, T., Matsuoka, Y. & Fujimori, S. Assessment of China's climate commitment and non-fossil energy plan towards 2020 using hybrid AIM/CGE model. Energy Policy 39, 2875–2887 (2011).

    Google Scholar 

  69. Fawcett, A. A. et al. Can Paris pledges avert severe climate change? Science 350, 1168–1169 (2015).

    CAS  Google Scholar 

  70. Thomson, A. M. et al. RCP4.5: a pathway for stabilization of radiative forcing by 2100. Climatic Change 109, 77–94 (2011).

    CAS  Google Scholar 

  71. van Vuuren, D. P. et al. RCP2.6: exploring the possibility to keep global mean temperature increase below 2 °C. Climatic Change 109, 95–116 (2011).

    Google Scholar 

  72. Deetman, S., Hof, A. F. & van Vuuren, D. P. Deep CO2 emission reductions in a global bottom-up model approach. Clim. Policy 15, 253–271 (2015).

    Google Scholar 

  73. Riahi, K., Grubler, A. & Nakicenovic, N. Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol. Forecast. Soc. Change 47, 887–935 (2007).

    Google Scholar 

  74. Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change 109, 33–57 (2011).

    CAS  Google Scholar 

  75. Luderer, G. et al. The economics of decarbonizing the energy system-results and insights from the RECIPE model intercomparison. Climatic Change 114, 9–37 (2012).

    Google Scholar 

  76. Bauer, N. et al. Global fossil energy markets and climate change mitigation – an analysis with REMIND. Climatic Change 136, 69–82 (2016).

    Google Scholar 

  77. Fais, B., Sabio, N. & Strachan, N. The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets. Appl. Energy 162, 699–712 (2016).

    Google Scholar 

  78. Arvesen, A., Bright, R. M. & Hertwich, E. G. Considering only first-order effects? How simplifications lead to unrealistic technology optimism in climate change mitigation. Energy Policy 39, 7448–7454 (2011).

    Google Scholar 

  79. Arvesen, A., Nes, R., Huertas-Hernando, D. & Hertwich, E. G. Life cycle assessment of an offshore grid interconnecting wind farms and customers across the North Sea. Int. J. Life Cycle Assess. 19, 826–837 (2014).

    CAS  Google Scholar 

  80. Kleijn, R. & van der Voet, E. Resource constraints in a hydrogen economy based on renewable energy sources: an exploration. Renew. Sustain. Energy Rev. 14, 2784–2795 (2010).

    Google Scholar 

  81. Gielen, D. J., Gerlagh, T. & Bos, A. J. M. MATTER 1.0 - A MARKAL Energy and Materials System Model Characterisation (Netherlands Energy Research Foundation ECN, 1998).

    Google Scholar 

  82. Frei, C. W., Haldi, P. A. & Sarlos, G. Dynamic formulation of a top-down and bottom-up merging energy policy model. Energy Policy 31, 1017–1031 (2003).

    Google Scholar 

  83. Böhringer, C. & Rutherford, T. F. Combining bottom-up and top-down. Energy Econ. 30, 574–596 (2008).

    Google Scholar 

  84. Suh, S. et al. System boundary selection in life-cycle inventories using hybrid approaches. Environ. Sci. Technol. 38, 657–664 (2004).

    CAS  Google Scholar 

  85. Schwanitz, V. J. Evaluating integrated assessment models of global climate change. Environ. Model. Softw. 50, 120–131 (2013).

    Google Scholar 

  86. Müller, D. B., Wang, T. & Duval, B. Patterns of iron use in societal evolution. Environ. Sci. Technol. 45, 182–188 (2011).

    Google Scholar 

  87. Casman, E. A., Morgan, M. G. & Dowlatabadi, H. Mixed levels of uncertainty in complex policy models. Risk Anal. 19, 33–42 (1999).

    Google Scholar 

  88. Liu, J. et al. Systems integration for global sustainability. Science http://doi.org/627 (2015).

  89. Bollinger, L. A., Nikolić, I., Davis, C. & Dijkema, G. P. J. Multimodel ecologies: cultivating model ecosystems in industrial ecology. J. Ind. Ecol. 19, 252–263 (2015).

    Google Scholar 

  90. De Koning, A., Huppes, G., Deetman, S. & Tukker, A. Scenarios for a 2 °C world: a trade-linked input–output model with high sector detail. Clim. Policy 16, 301–317 (2015).

    Google Scholar 

  91. Wiebe, K. S. The impact of renewable energy diffusion on European consumption-based emissions. Econ. Syst. Res. 28, 133–150 (2016).

    Google Scholar 

  92. von Stechow, C. et al. Integrating global climate change mitigation goals with other sustainability objectives: a synthesis. Annu. Rev. Env. Resour. 40, 363–394 (2015).

    Google Scholar 

  93. Schäfer, A. Structural change in energy use. Energy Policy 33, 429–437 (2005).

    Google Scholar 

Download references

Acknowledgements

A.A. received support from the Research Council of Norway through the Centre for Sustainable Energy Studies (contract 209697). The research was conducted without involvement of the funding sources. The authors thank V. Krey (MESSAGE), G. Luderer (REMIND), and S. Fujimori (AIM/CGE) for providing additional information and for commenting on earlier versions of this work. The authors thank E. Ó Broin for helping to review the AIM-CGE model and for his contribution to framing our review approach. The authors remain solely responsible for the content of this Perspective and for possible mistakes in the model review.

Author information

Authors and Affiliations

Authors

Contributions

S.P. and E.H. designed the approach, A.A., K.S., and S.P. performed the review, and all authors contributed to writing the paper.

Corresponding author

Correspondence to Stefan Pauliuk.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Industrial ecology in integrated assessment models (PDF 2469 kb)

Supplementary information

Industrial ecology in integrated assessment models (XLSX 346 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pauliuk, S., Arvesen, A., Stadler, K. et al. Industrial ecology in integrated assessment models. Nature Clim Change 7, 13–20 (2017). https://doi.org/10.1038/nclimate3148

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nclimate3148

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing