OPTIMAL CLIMATE POLICY IN 3D: MITIGATION, CARBON REMOVAL, AND SOLAR GEOENGINEERING

We introduce solar geoengineering (SG) and carbon dioxide removal (CDR) into an integrated assessment model to analyze the trade-offs between mitigation, SG, and CDR. We propose a novel empirical parameterization of SG that disentangles its efficacy, calibrated with climate model results, from its direct impacts. We use a simple parameterization of CDR that decouples it from the scale of baseline emissions. We find that (a) SG optimally delays mitigation and lowers the use of CDR, which is distinct from moral hazard; (b) SG is deployed prior to CDR while CDR drives the phasing out of SG in the far future; (c) SG deployment in the short term is relatively independent of discounting and of the long-term trade-off between SG and CDR over time; (d) small amounts of SG sharply reduce the cost of meeting a [Formula: see text]C target and the costs of climate change, even with a conservative calibration for the efficacy of SG.

The mortality cost of carbon

Many studies project that climate change can cause a significant number of excess deaths. Yet, in integrated assessment models (IAMs) that determine the social cost of carbon (SCC) and prescribe optimal climate policy, human mortality impacts are limited and not updated to the latest scientific understanding. This study extends the DICE-2016 IAM to explicitly include temperature-related mortality impacts by estimating a climate-mortality damage function. We introduce a metric, the mortality cost of carbon (MCC), that estimates the number of deaths caused by the emissions of one additional metric ton of CO2. In the baseline emissions scenario, the 2020 MCC is 2.26 × 10‒4 [low to high estimate −1.71× 10‒4 to 6.78 × 10‒4] excess deaths per metric ton of 2020 emissions. This implies that adding 4,434 metric tons of carbon dioxide in 2020—equivalent to the lifetime emissions of 3.5 average Americans—causes one excess death globally in expectation between 2020-2100. Incorporating mortality costs increases the 2020 SCC from $37 to $258 [−$69 to $545] per metric ton in the baseline emissions scenario. Optimal climate policy changes from gradual emissions reductions starting in 2050 to full decarbonization by 2050 when mortality is considered.

Improving the decision-making in DICE: self-adaptive climate policies to handle explicit uncertainty and adaptation modelling

<p>DICE (Dynamic Integrated Climate Economy) and other cost-benefit integrated assessment models are used to study the economically optimal climate policy or to evaluate economic performance of alternative policies, such as 2°C compliant emission trajectories.</p><p>Recently, DICE has been updated to provide economically optimal climate policies keeping global warming in line with the Paris Agreement. Yet, explicit uncertainty and adaptation modelling are still overlooked. Introducing these components requires a transition from the traditional perfect-foresight static decision-making framework to a dynamic one, able to change strategy in order to react to the realization of uncertainties.</p><p>In this work, starting from the updates proposed by Hansel et al. (2020), we present an updated DICE model that: i) explicitly represents adaptation in the form of temporary and long-term adaptation investment; ii) explicitly describes stochastic, parametric and structural uncertainty over the physical and socio-economic components of the model including adaptation efficiency and climate damages specification; iii) leverages self-adaptive control policies to implement a more realistic decision-making scheme that allows to adjust climate policy after that new information arises.</p><p>Results show that the self-adaptive policies allow for a reduction in the discrepancy between economically optimal climate policy and the 2°C temperature target set with the Paris Agreement, which resurfaces when introducing adaptation, also in presence of uncertainty. When using self-adaptive policies, average adaptation costs remain low and, thanks to the ability to modulate adaptation choices depending on the scenario eventually unfolding, also climate damages are maintained at a low level. As a result, more economic resources are made available for mitigation in the short-term resulting in a reduced temperature increase in 2100 for a same level of welfare.</p>

Optimal Climate Policy When Damages are Unknown

Integrated assessment models (IAMs) are economists’ primary tool for analyzing the optimal carbon tax. Damage functions, which link temperature to economic impacts, have come under fire because of their assumptions that may be incorrect in significant but a priori unknowable ways. Here I develop recursive IAM frameworks to model uncertainty, learning, and concern for misspecification about damages. I decompose the carbon tax into channels capturing state uncertainty, insurance motives, and precautionary saving. Damage learning improves ex ante welfare by $750 billion. If damage functions are misspecified and omit the potential for catastrophic damages, robust control may be beneficial ex post. (JEL H23, Q54, Q58)

Optimal climate policy when damages are unknown

Integrated assessment models (IAMs) are economists’ primary tool for analyzing the optimal carbon tax. Damage functions, which link temperature to economic impacts, have come under fire because of their assumptions that may be incorrect in significant, but a priori unknowable ways. Here I develop recursive IAM frameworks to model uncertainty, learning, and concern for misspecification about damages. I decompose the carbon tax into channels capturing state uncertainty, insurance motives, and precautionary saving. Damage learning improves ex ante welfare by $750 billion. If damage functions are misspecified and omit the potential for catastrophic damages, robust control may be beneficial ex post.