In This Article Expand or collapse the "in this article" section Integrated Assessment Models (IAMs) for Climate Change

  • Introduction
  • Overviews and Surveys
  • Moderate-Sized IAMs for Climate Change by Individual Scholars
  • National-Level IAMs for Climate Change
  • Regional-Scale Models
  • IAMs for Climate Change and Environmental Economics
  • IAMs for Climate Change in Education

Environmental Science Integrated Assessment Models (IAMs) for Climate Change
Zili Yang, Yi-Ming Wei, Zhifu Mi
  • LAST REVIEWED: 29 July 2020
  • LAST MODIFIED: 26 August 2020
  • DOI: 10.1093/obo/9780199363445-0043


Integrated assessment models (IAMs) for climate change refers to a broad category of research approaches in climate change. Climate change is the most complicated global environmental problem. By the very nature of climate change, research has to be interdisciplinary and multifaceted. IAM is the mainstream methodological approach in climate change research. Most researchers in climate change utilize IAMs directly or indirectly. IAMs draw knowledge and strengths from various disciplines related to climate change; contributions from each discipline rely on the mathematical representations of certain relationships connected to climate change; disciplinary components are linked through a unified modeling platform(s). In particular, IAMs for climate change usually involve social-economic components as well as natural sciences components. The key linkages in IAM platforms are anthropogenic greenhouse gas (GHG) emissions in climate systems and climate change impacts on social-economic systems. The outputs of IAMs are numerical simulation results based on assumptions, historical data, and scenario designs. IAMs are widely used in assessing various GHG mitigation policies and climate impacts. In fact, conclusions in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports are drawn substantially from numerous IAMs. IAMs for climate change started in the late 1980s. Since then, IAMs for climate change have developed into a full-fledged interdisciplinary research field that involves hundreds of models, thriving online resources, and thousands of academic publications and policy reports around the world. IAM for climate change, as an interdisciplinary research approach, has received recognition by mainstream disciplines. The Dynamic Integrated model of Climate and the Economy (DICE) and the Regional Integrated model of Climate and the Economy (RICE)—two IAMs for climate change—are part of the core contributions in William Nordhaus’s Nobel Prize in Economic Sciences in 2018.

Overviews and Surveys

IAM for climate change is a methodological approach as well as a sustainable research platform for many researchers. A well-developed IAM usually has undergone revisions, improvements, and recalibrations over time. IAMs are subject to constant reviews and critiques by scholars and IAM modelers. Critical evaluations of IAMs appeared in the literature beginning in the mid-1990s. The surveys in this section reveal the development and scope of IAMs since that period. Because IAMs represent broad arrays of research accumulations, comprehensive and unbiased overviews and surveys are difficult. The articles in this section critically survey IAMs at progressing development junctions and from different perspectives. Some authors of articles in this section are well-known IA modelers themselves. Their survey articles are based on their modeling experiences with IAM for climate change, such as Nordhaus 2013, an article from the perspective of the Regional Integrated model of Climate and the Economy (RICE); Dowlatabadi 1995 and Morgan and Dowlatabadi 1996 are largely based on early experiences with the Integrated Climate Assessment Model (ICAM). The other articles adopt more comprehensive approaches in their inclusion of models for comparisons and critiques. Weyant 2017 summarizes the achievements of IAMs for climate change, presented by a longtime organizer of the Energy Modeling Forum (EMF; see EMF 29, cited under American Models) and the IAM Consortium. On the other hand, Pindyck 2017 presents some pitfalls and caveats in the applications of IAMs from the perspective of economics. Pauliuk, et al. 2017 points out an omission in major IAMs and suggests the approach of improvement.

  • Ackerman, F., S. deCanio, R. Howarth, and K. Sheeran. 2009. Limitations of integrated assessment models of climate change. Climatic Change 95.3–4: 297–315.

    DOI: 10.1007/s10584-009-9570-x

    This article is a critical evaluation of IAMs for climate change by questioning some key assumptions in IAMs for climate change.

  • Dowlatabadi, H. 1995. Integrated assessment models of climate change: An incomplete overview. Energy Policy 23.4–5: 289–296.

    DOI: 10.1016/0301-4215(95)90155-Z

    This paper is an early introduction and summary of IAM for climate change, which took shape as a research methodology in climate change at the time.

  • Kelly, D. L., and C. D. Kolstad. 1999. Integrated assessment models for climate change control. In International yearbook of environmental and resource economics 1999/2000: A survey of current issues. Edited by H. Folmer and T. Tietenberg, 171–197. Cheltenham, UK: Edward Elgar.

    This paper provides a clear cross-model comparison in model structure, solution concepts, and outputs.

  • Morgan, M. G., and H. Dowlatabadi. 1996. Learning from integrated assessment of climate change. Climatic Change 34.3–4: 337–368.

    DOI: 10.1007/BF00139297

    This article assesses the role of IAM in climate change research, largely based on the early experiences with the ICAM model at Carnegie-Mellon University.

  • Nordhaus, W. 2013. Integrated economic and climate modeling. In Handbook of computable general equilibrium modeling. Vol. 1. Edited by P. B. Dixon and D. Jorgenson, 1069–1131. Amsterdam: Elsevier.

    DOI: 10.1016/B978-0-444-59568-3.00016-X

    This article (chapter 16) introduces general principles of IAM from the perspective of an economist and a pioneer of IAM. It focuses on DICE/RICE models and is not a complete survey on the literature.

  • Pauliuk, S., A. Arvesen, K. Stadler, and E. G. Hertwich. 2017. Industrial ecology in integrated assessment models. Nature Climate Change 7:13–20.

    DOI: 10.1038/nclimate3148

    This paper describes lack of material cycles and recycling as a flaw in IAMs. The authors introduce the industrial ecology (IE) approach as the means to overcome the identified flaw.

  • Pindyck, R. S. 2017. The use and misuse of models for climate policy. Review of Environmental Economics and Policy 11:100–114.

    DOI: 10.1093/reep/rew012

    This article presents a critique of IAMs by a mainstream economist. It points out the weakness of IAMs from a methodological standpoint and questions their usefulness in policy analysis. The arguments are helpful to IAM modelers, even if they do not agree with the author’s arguments completely.

  • van Vuuren, D., J. Lowe, E. Stehfest, et al. 2011. How well do integrated assessment models simulate climate change? Climatic Change 104.2: 255–285.

    DOI: 10.1007/s10584-009-9764-2

    This paper analyzes the sources of different outcomes from complicated IAM systems and the implications of such differences in climate change policies.

  • Weyant, J. 2017. Some contributions of integrated assessment models of global climate change. Review of Environmental Economics and Policy 11:115–137.

    DOI: 10.1093/reep/rew018

    This survey article reviews the use of scale IAMs over the previous thirty years. The author classifies global IAMs in one of two categories: detailed process IAMs and aggregate benefit-cost analysis (BCA) IAMs. The strengths and weaknesses of each category are evaluated.

  • Weyant, J., O. Davidson, H. Dowlabathi, et al. 1996. Integrated assessment of climate change: An overview and comparison of approaches and results. In Climate change 1995: Economic and social dimensions of climate change. Edited by J. Bruce, H. Yi, and E. Haites, 367–439. Cambridge, UK: Cambridge Univ. Press.

    This paper outlines classifications of early IAMs based on modeling methodologies and approaches, indicating the strengths and weakness of each approach.

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