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Institutional Subscription. Free Shipping Free global shipping No minimum order. Summary 1. Summary 2. Summary 3. Complexity and Uncertainty: Rethinking the Modelling Activity. Environmental Policy Aid under Uncertainty. Intelligent Environmental Decision Support Systems. Computational Air Quality Modelling. Community Modelling State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions.

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Practice and perspectives in the validation of resource management models

You are connected as. Connect with:. Use your name:. Thank you for posting a review! We value your input. Share your review so everyone else can enjoy it too. Your review was sent successfully and is now waiting for our team to publish it. Regarding the relationship between science and politics, a common view in the contemporary STS literature is that the generation of scientific knowledge, policymaking, and social order all exist in a coproductive relationship with each other.

These issues then become subject to the implicit and socially contingent judgments of experts rather than a wider political discussion among the numerous and heterogenous stakeholders of climate change. Ultimately, modeling decisions may narrow prematurely the content of policy deliberations. This exclusion raises questions regarding the democratic legitimacy of expert assessments in policymaking.

In this section, we discuss examples of coupled epistemic—ethical issues from the IAM literature. Krueger et al. Although presented in sequential order, in practice, the choices made at each point are understood to be interdependent and iterative. The perceptual model underlying IAMs is the structured and qualitative understanding of the climate change problem, including its causes, processes, and consequences adapted from Beven This understanding includes a particular framing of the problem that the model is intended to address.

The market failure framing also makes it plausible to ask very focused research questions about the optimal carbon price that is expected to fix the market distortion. This price is set by the intersection of marginal climate damages with the marginal cost of abatement. From an ethical perspective, the market failure framing, which effectively turns the atmosphere into a commodity, assumes high substitutability between human goods such as technology and capital, and nonhuman goods, including biodiversity, ecosystems, and landscapes.

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First, pricing carbon emissions directly attributes causal responsibility to the emitter. Hence, the logic of this approach does not justify a distinction between subsistence emissions caused by the poor to achieve a decent standard of living and luxury emissions caused by the rich, as suggested by some climate ethicists. At this stage in the modeling process, the perceptual model is translated into a formal model structure, that is, a set of mathematical equations that formalize the relationships between the elements and processes of the modeled system.

Specifying a model structure for IAMs involves choices about which elements and processes of the climate and socioeconomic systems should be included. In particular, modelers may need to weigh model completeness against model reliability when deciding whether to include poorly understood aspects of climate change that are nevertheless expected to have significant impacts on model results.

For example, it is generally considered likely that climate change will cause or worsen violent conflicts, although the empirical basis for quantifying the causal mechanism between climate change and conflicts is still small. Excluding climate change effects on violent conflicts from the model prevents any future victims of such conflicts who will likely not have contributed to causing climate change from being recognized in the policy process and, ceteris paribus , likely results in an undervaluation of climate change damages and possibly misleading policy recommendations.

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One of these IAMs is the model used by Nordhaus in a study from ; the other is used in a more recent study by McInerney and Keller from Both are optimizing models, meaning that they maximize social welfare over time. While the Nordhaus model runs without additional constraints, McInerney and Keller introduce an additional constraint on optimization. Their model requires that the probability of a particular event, namely the irreversible collapse of the North Atlantic meridional overturning circulation, must never exceed a predetermined limit.

Both models adopt a utilitarian objective function, which generally ignores disparities in welfare distribution between the rich and the poor. The threshold constraint that is introduced in McInerney and Keller's model implies ethical judgments about 1 a specific outcome that should be avoided partly independent of economic costs and 2 an acceptable probability of this outcome occurring anyway. These decisions would be ethically controversial even if the science of irreversible system thresholds were settled.

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Specifying a model structure also involves decisions about the level of regional disaggregation, that is, the number of geographical regions in the model, what Morgan and Henrion 48 call a domain parameter. Other domain parameters include model time horizon and time increment.


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While domain parameters are often neglected in model uncertainty analysis, their impacts on model results may be considerable. In particular, some IAMs incorporate equity weights to make the cost of climate change more comparable across poor and rich regions. Therefore, the degree of regional disaggregation in the model and the methodology that is used to define model regions affect the fairness of the resulting weighting scheme.

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Dennig et al. The estimation of parameter values involves choices, too. In the IAM literature to date, the most explicit discussion of coupled epistemic—ethical choices concerns one specific model parameter: the social discount rate used to calculate the present value of future consumption losses due to climate change.

Economists treat climate change mitigation efforts as investments in future consumption, , and discounting acknowledges that consumption may be valued differently depending on when it occurs. In the context of climate change, discounting has ethical importance; it reflects the weight that the current generation assigns to the welfare of future generations in relation to its own, considering that current economic activity is causing future climate impacts. A common technique for determining the social discount rate in IAMs is the Ramsey optimal growth model, which defines it as a function of the pure rate of time preference, the consumption elasticity of marginal utility, and the projected growth rate of consumption.

The pure rate of time preference is essentially a measure of impatience and it measures the loss of utility that is experienced simply because consumption occurs in the future rather than today. The consumption elasticity describes how quickly the marginal utility of consumption declines with increasing consumption. It essentially measures the value of a dollar's worth of consumption to the poor versus the rich.

Economists commonly pursue either a descriptive or a prescriptive approach to define the social discount rate. The former, as promoted for example by Arrow et al.


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  • The intention is to treat the rate of time preference as an uncertain empirical quantity rather than the subject of value diversity. There is no consensus in the economics literature on which approach is preferable. However, several philosophers argue that, independent of the method chosen, the choice is never ethically neutral. Baum illustrates the inevitable ethical judgments involved in observing, measuring, and aggregating society's discounting behavior.

    Fischhoff argues that any use of market prices in policy assessments always implies the ethically relevant assumption that the observed market is functioning properly, that is, that all relevant externalities are priced in. IAMs can theoretically deliver various types of model output, but as mentioned earlier see Box 1 , the most commonly presented results are optimal global abatement targets, carbon tax rates, and SCC estimates. In the face of scientific and ethical uncertainty, these output choices have both epistemic and ethical importance because of the information they conceal.

    The SCC, for example, is a single monetary impact metric that is aggregated from already highly aggregated estimates of climate damages across time and regions. Aggregation obscures the boundaries between groups and complicates the determination of who exactly is bearing the costs of climate change. This also complicates the direct comparison of outputs from different studies because the variances in internal model processes and assumptions remain invisible. Having identified examples of concrete coupled epistemic—ethical choices in the development of IAMs, we now turn to identifying concrete examples of the political dimension of uncertainty management in IAMs as reported in the even more dispersed literature.

    These have also been identified by scholars in STS as possible points for politics to enter the scientific process. However, the scholarly discussion of the politicization of IAMs, and uncertainty in IAMs in particular, is small and evidence mostly anecdotal. In the following, we review some empirical studies from the somewhat more systematic research on the politics surrounding the development of climate models.

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    Although we are not suggesting that the two types of models are equivalent, these studies may provide entry points for future research on IAMs. Krueck and Borchers perform an institutional comparison between two climate modeling centers in Europe to investigate how the modelers deal with the challenge to generate policy relevant knowledge about a politically charged issue in the face of scientific uncertainty and value diversity.

    Shackley et al. Shackley and colleagues argue that these social rather than scientific factors have led to the exclusion of other model types from the toolbox available for policy advice. Again, Tol's quote cited above indicates that similar dynamics may be at work in the IAM community.

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    However, the conclusions that Shackley et al. The dominance of GCMs certainly appears to be a question of scale of application. As one of the reviewers of this article pointed out, much of the policy work in the IPCC, where no regional detail was required, has been based on simpler climate models, not GCMs. In another empirical study, Shackely et al. The authors look at one technical modeling issue, the use of flux adjustments in coupled Atmosphere—ocean GCMs, and its politicization in policy debates. Flux adjustments are a possible way for modelers to compensate drifts in model outputs that often occur when atmosphere and ocean models are linked.

    The drift indicates underlying errors in the model, which are not fixed by the flux adjustment; but this addresses the symptoms. Shackely and colleagues distinguish between a pragmatic and a purist culture. In the following, we take a more detailed look at coproduction in modeling for policymaking as a useful entry point for future empirical research on the integrated assessment modeling process. In this sense, IAMs are influenced by and influence elements of social order, and this coproduction creates and maintains stability in the understanding and management of uncertainty with regards to all three dimensions—epistemic, ethical, and political.

    Risbey et al.