Climate Change Economics

Integrated Assessment Models (IAMs) require parameterization of both economic and climatic processes. The latter includes Equilibrium Climate Sensitivity (ECS), or the temperature response to doubling CO₂ levels, and Ocean Heat Uptake (OHU) efficiency. ECS distributions in IAMs have been drawn from climate model runs that lack an empirical basis, and in Monte Carlo experiments may not be constrained to consistent OHU values. Empirical ECS estimates are now available, but have not yet been applied in IAMs. We incorporate a new estimate of the ECS distribution conditioned on observed OHU efficiency into two widely used IAMs. The resulting Social Cost of Carbon (SCC) estimates are much lower than those from models based on simulated ECS parameters. In the DICE model, the average SCC falls by approximately 40–50% depending on the discount rate, while in the FUND model the average SCC falls by over 80%. The span of estimates across discount rates also shrinks substantially.

We consider a carbon emissions tax announced today, but implemented after a known time-lag. Before implementation, the announcement induces higher emissions than without intervention. In welfare terms, this adverse announcement effect could more than outweigh the gain after tax implementation. We quantify a “critical lag” such that a shorter (longer) implementation lag is a welfare gain (loss) over no-intervention. We identify resource scarcity as the main driver for a short critical lag. The model is a global Ramsey Model extended by an exhaustible carbon resource and linked to a climate model.

The permafrost carbon feedback (PCF) is not currently taken into account in economic assessments of climate change, yet it could have important implications for the social cost of carbon (SCC) and the associated choice of the optimal greenhouse gas emission pathway. Although this feedback is still imperfectly known, there are enough estimates of its potential strength to now include it in our assessments. In this paper, I present a model of the PCF and integrate this feedback in the DICE Integrated Assessment Model to examine its consequences. I find that doing so increases the SCC by 10–20% in the baseline scenario, but that this impact is much more significant in the case of a damage function which is more reactive to very high temperature changes, and can reach up to 220%. It follows that setting industrial emission targets without taking into account this feedback would lead to excessive atmospheric carbon: I find that it increases the optimal emission control rate by circa 5% points on average over the period 2015–2110 and that this difference becomes much more significant when the constraint of limiting the increase in global mean temperature to $+2^{\circ }\mathrm{C}$ or $+1.5^{\circ }\mathrm{C}$ is added to the model.

The social cost of carbon (SCC) is a monetary estimate of global climate change damages to society from an additional unit of carbon dioxide (CO₂) emissions. SCCs are used to estimate the benefits of CO₂ reductions from policies. However, little is known about the modeling underlying the values or the implied societal risks, making SCC estimates difficult to interpret and assess. This study performs the first in-depth examination of SCC modeling using controlled diagnostic experiments that yield detailed intermediate results, allow for direct comparison of individual components of the models, and facilitate evaluation of the individual model SCCs. Specifically, we analyze DICE, FUND, and PAGE and the multimodel approach used by the US Government. Through our component assessments, we trace SCC differences back to intermediate variables and specific features. We find significant variation in component-level behavior between models driven by model-specific structural and implementation elements, some resulting in artificial differences in results. These elements combine to produce model-specific tendencies in climate and damage responses that contribute to differences observed in SCC outcomes — producing PAGE SCC distributions with longer and fatter right tails and higher averages, followed by DICE with more compact distributions and lower averages, and FUND with distributions that include net benefits and the lowest averages. Overall, our analyses reveal fundamental model behavior relevant to many disciplines of climate research, and identify issues with the models, as well as the overall multimodel approach, that need further consideration. With the growing prominence of SCCs in decision-making, ranging from the local-level to international, improved transparency and technical understanding is essential for informed decisions.