Postdoctoral Fellow, ISSP
Global Governance PhD candidate (ABD) at the Balsillie School of International Affairs, University of Waterloo
The Canadian government has expressed a strong commitment to grounding its action on climate change in fact-based decision-making and robust science. Computer-based climate-economy models have become standard tools for aiding decisions on climate policy. As such, these models offer an important and revealing example of the science- policy interface that the ISSP works in.
How do these models work and how exactly does modeled knowledge feed into climate policy in practice?
In my doctoral research, I study integrated assessment models (IAMs), a particular family of climate-economy models. IAMs are computer simulation models that combine mathematical representations of the climate system, the economic system, and the interactions between the two. The explicit purpose of IAMs is to inform policy. Some IAMs calculate optimal targets for greenhouse gas emissions reductions; others compare the economic and environmental impacts of alternative climate policy scenarios. A common application of IAMs in national climate policy is the calculation of the social cost of carbon (SCC). This indicator is an important input to regulatory impact analysis; it measures the damages caused by the release of one additional ton of carbon into the atmosphere.
In my PhD thesis, I explore in great depth the inputs and outputs of IAMs—the assumptions, values, and beliefs that become manifested in the models’ mathematical equations and in model results. But there is another important question I have not yet been able to address in my research: how do policymakers actually use climate-economic modeling in practice?
It is of course naive to assume that model outputs directly inform decisions. The policy process is complex and political; scientific knowledge is merely one of many potentially influential factors. In particular, climate change is both a technically complex and politically controversial issue. The high degree of scientific uncertainty about the magnitude and timing of climate change impacts requires modelers to make numerous choices in the development of IAMs. Hence, rather than assuming a linear direction of influence from climate-economic models to policy, it is more realistic to assume mutual influences between these two spheres, as I argue in my dissertation. As a result, the models are neither objective nor value-free.
Against this background, it is also not clear what role modeled knowledge even should have in decision-making, relative to other types of knowledge such as community and indigenous knowledge that may legitimately shape policy, and that may better represent stakeholder views on the important ethical issues arising from climate change.
Climate change’s stakeholders are diverse and geographically dispersed. Those most affected by climate impacts—populations in developing countries and small island states—generally are not accountable for significant amounts of greenhouse gas emissions. The ethical implications of this uneven allocation of historical responsibility for climate change and expected climate damages are difficult to quantify, and conventional IAMs typically do not address this issue.
Similarly, since climate change has a long time horizon, stakeholders include future generations who cannot be present at today’s negotiation tables. Climate-economic models address this issue by defining a discount rate that essentially governs how much weight future impacts are given in today’s decisions. But how to specify this discount rate is one of the most controversial topics in the climate economics literature. What seems clear is that any assumption made on discounting has significant consequences for intergenerational justice.
So, how can policymakers balance the objective of evidence-based decision-making with the objective of inclusive stakeholder representation, given the technical, political, and ethical complexity of the climate change problem? I suggest that using computer models as decision aids offers great opportunities for systematically exploring and coherently evaluating complex policy problems. But I also believe that one of the models’ most important contributions lies in highlighting the aspects of the problem that cannot be solved through mathematical equations alone. The really hard questions in climate policy, those concerning inter- and intragenerational justice and stakeholder representation, still require fair and inclusive deliberations.