The Uphill Battle to Reduce CO2 Emissions May Be Twice as Steep as Previously Thought

When weather forecasters predict another polar vortex for the Midwest this winter, global warming doesn’t sound so bad. While warming may create both winners and losers in the short-term, the Intergovernmental Panel on Climate Change (IPCC) states, “Continued emission of greenhouse gases will cause further warming and long-lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive and irreversible impacts for people and ecosystems.”

The pivotal questions are: When and by how much do we need to reduce carbon dioxide (CO₂) emissions in order to avoid the IPCC’s dire projections? These questions are not easy to answer and depend on our assumptions about the interactions between economic activity, emissions, and surface temperature responses.

These interactions are evaluated through integrated assessment models. One of the most widely cited is William Nordhaus’ DICE model, used to determine the US social cost of carbon. In a new study, Benjamin Crost and Christian Traeger use a modified version of the DICE model, in which assumptions about consumer behavior better reflect observed market outcomes, to compute the optimal carbon tax and abatement rate over a 100-year period. The abatement rate is the rate at which carbon dioxide emissions are reduced over a given period. Their results include two noteworthy conclusions: (1) Both the optimal carbon tax and the present day abatement rate should be double what Nordhaus’ model predicts, and (2) the outcomes of a policy are significantly affected by uncertainty levels across parameters.

The majority of large-scale climate-economy models, including Nordhaus’ DICE model, are deterministic—in other words, the outcome is completely determined by the model’s parameters and initial conditions. Crost and Traeger modify Nordhaus’ model to reflect a more realistic world in which a random element influences the relationship between climate change and the economy. The model assumes a policy maker who must choose the optimal policy (e.g., a carbon tax or CO₂ abatement level) despite uncertainty about future damage caused by a temperature increase. They define an optimal policy as one that maximizes average social welfare over an infinite time horizon, rather than maximizing economic production in the present.

Social welfare and consumer preferences are determined by risk aversion and the desire to consume evenly over a period of time rather than experience consumption fluctuations. Standard climate-economy models assume that risk aversion corresponds to consumption smoothing and creates a joint variable representing both factors. According to Crost and Traeger, observed preferences do not support this assumption. Thus, the authors separate risk aversion and consumption smoothing into two distinct inputs.

In addition to calculating optimal carbon taxes and abatement levels, the authors seek to better understand how different levels of uncertainty influence predicted production levels. Their analysis of uncertainty demonstrates that not all uncertainty is created equal. Using an incorrect value to describe the steepness of the damages vs. temperature curve reduces social welfare more, on average, than choosing an incorrect value to describe the damage level of a 1°C increase.

By adding an intrinsic random element and separating the risk aversion and consumption smoothing parameters, this new climate-economy model is more consistent with observed consumer preferences. In comparison with Nordhaus’ widely cited DICE model, Crost and Traeger’s results nearly double the optimal carbon tax and abatement levels. Regardless of whether these results accurately reflect the climate-economy relationship, understanding where we have the most uncertainty is important and should be a major input in the choice between a policy that sets a carbon tax and one that sets an abatement level.

Article Source: Benjamin Crost and Christian P. Traeger, Optimal CO₂ mitigation under damage risk valuation, Nature Climate Change, 2014.

Featured Photo: cc/(St Stev)