Frameworks for Dealing with Climate and Economic Uncertainties in Integrated Assessment Models

Prieg, Lydia and Yumashev, Dmitry (2020) Frameworks for Dealing with Climate and Economic Uncertainties in Integrated Assessment Models. In: Integrated Assessment Models and Others Climate Policy Tools. Oeconomia Editions. ISBN 9791092495126 (In Press)

Full text not available from this repository.


IAMs connect physical and social science models to address cross-disciplinary questions, such as how does climate affect economies. There are different types of IAMs. One category of IAMs, for example, derives scenarios for future population, economies, technology and greenhouse gas (GHG) emissions, and explores how these may influence climate variables and, subsequently, the biosphere. These IAMs tend to be large, complex and computationally intensive. As a result, they are typically deterministic, as there is often insufficient information available to define probability distributions for the thousands of parameters of the model. Even if this could be done, computational power is often not sufficient to run large numbers of simulations with varying parameters. Examples of such IAMs include the Integrated Model to Assess the Global Environment (IMAGE) (van Vuuren et al., 2011, p. 6) and the Global Change Assessment Model (GCAM) (Wise et al., 2009). A different category of IAMs primarily estimates the economic costs and benefits of climate change, and then uses cost-benefit analysis (CBA) to assess the relative desirability of different GHG emissions as well as adaptation policies. These IAMs tend to be much smaller and simpler, which means that uncertainties can be explored via Monte Carlo analysis. IAMs in this group sometimes use GHG and socioeconomic scenarios produced by IAMs in the previous category as exogenous inputs, and then generate their own estimates of temperature, sea-level rise (SLR) and the associated economic impacts. Alternatively they may generate the input scenarios themselves using simple internal models, and then use these in other components of the model. Popular examples include the Dynamic Integrated Climate-Economy model (DICE) (Nordhaus, 2017), the Climate Framework for Uncertainty, Negotiation and Distribution (FUND) (Anthoff and Tol, 2014) and Policy Analysis of the Greenhouse Effect (PAGE) (Hope, 2013). This second group of IAMs will often focus on estimating the social cost of carbon (SCC), which is the discounted economic impact of emitting an additional tonne of carbon today. William Nordhaus, the creator of DICE, for example, called the SCC “The most important single economic concept in the economics of climate change” (Nordhaus, 2017). The SCC is calculated by running the same climate and socioeconomic scenario, for example business as usual or 2°C in 2100, twice; only, in one case, assuming additional CO2 emissions in the starting period. The SCC will, thus, be different for different scenarios, although the variation is often limited (Hope and Newbery, 2006). With recognition of its limitations, the SCC is sometimes cautiously put forward as the possible tax that should be applied on carbon emissions to correct for associated climate change externalities, i.e. the costs from carbon emissions not included in the prices of goods and services produced by carbon-emitting industries. For example, see Anthoff and Tol (2013) and Hope and Newbery (2006). Modelling how two highly complex systems, the climate and the macroeconomy, will evolve individually, let alone when they interact with one another, is plagued with uncertainties. As will be explored in section 5 of this chapter, results generated by IAMs in the first category are typically used in a manner that reflects the deep uncertainties present. The builders and users of IAMs in the second category, however, place greater faith in the ability to quantify uncertainties, and their results are generally interpreted in this light. For example, cost-optimal GHG emissions trajectories and the mean SCC are often estimated. This chapter looks at the frameworks utilised to analyse the outputs of IAMs, particularly those in the second category, and alternative frameworks for exploring results generated by IAMs in the second category that are more appropriate for decision-making under deep uncertainty. Section 2 summarises sources of uncertainty and how they are quantified. Section 3 highlights broader problems with modelling and predicting complex nonlinear systems. By reflecting on why models are built, section 4 explores how imperfect models can still have academic value if used appropriately. Section 5 suggests alternative frameworks for communicating and using results generated by IAMs in the second category in light of the preceding analysis. Section 6 concludes.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
Deposited By:
Deposited On:
24 Jun 2020 11:35
In Press
Last Modified:
25 Jul 2021 06:41