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The paper discusses optimal carbon taxation in an analytic quantitative integrated assessment model (IAM). The model links IAM components and parametric assumptions directly to their policy impacts. The paper discusses the distinct tax impact of carbon versus temperature dynamics and uses the see-through model to illustrate various aspects of IAM calibrations including the differentiation between consumption and investments goods. Novel to analytic IAMs are the explicit temperature dynamics, a general economy, energy sectors including capital, various degrees of substitutability across energy sources, an approximation of capital persistence, and objective functions that include CES preferences and population weighting. ACE opens the door to tractable forward-looking stochastic modeling and dynamic strategic interactions in complex IAMs, explored in accompanying work.

We analyze the policy implications of aversion to Knightian uncertainty (ambiguity) about the possibility of tipping points. We demonstrate two channels through which uncertainty aversion affects optimal policy in the general setting. The first channel relates to the policy's effect on the probability of tipping, and the second channel to its differential impact in the pre- and post-tipping regimes. We then extend a recursive dynamic model of climate policy and tipping points to include uncertainty aversion. Numerically, aversion to Knightian uncertainty in the face of an ambiguous tipping point increases the optimal tax on carbon dioxide emissions, but only by a small amount.

The present paper introduces a novel idea of what constitutes risk attitude, how we can represent it, and how we can compare risk attitude across agents with differing tastes. In contrast to the Arrow--Pratt measure, it links directly to preferences on the multidimensional consumption space and is independent of the way in which we measure consumption. The paper derives a corresponding preference representation and applied insights governing Epstein-Zin preferences and asset pricing models. In particular, it suggests why the popular long-run risk model finds estimates of the elasticity of intertemporal substitution constrasting sharply with the rest of macroeconomics.

Global warming could trigger irreversible regime shifts—‘tipping points’—in the climate system. This study analyses climate policy in the presence of a potential domino effect resulting from the interaction of such tipping points.

This paper is a revised version of: https://ssrn.com/abstract=2667972. The Analytic Climate Economy (ACE) closes a gap between analytic climate change assessments and quantitative numeric integrated assessment models (IAMs) used in policy advising. Its closed-form solution links IAM components and parametric assumptions directly to their policy impacts. Its analytic nature overcomes Bellman's curse of dimensionality for a wide range of stochastic processes. ACE shows that uncertainty flips the main drivers of the carbon tax. Uncertainty also makes IAMs even more sensitive to the discount rate and its composition. Under a recent survey's median estimate for pure time preference, uncertainty almost triples the optimal tax.

Over the last few decades, integrated assessment models (IAM) have provided insight into the relationship between climate change, economy, and climate policies. The limitations of these models in capturing uncertainty in climate parameters, heterogeneity in damages and policies, have given rise to skepticism about the relevance of these models for policy making. IAM community needs to respond to these critics and to the new challenges posed by developments in the policy arena. New climate targets emerging from the Paris Agreement and the uncertainty about the signatories’ commitment to Nationally Determined Contributions (NDCs) are prime examples of challenges that need to be addressed in the next generation of IAMs. Given these challenges, calculating the social cost of carbon requires a new framework. This can be done by computing marginal abatement cost in cost-effective settings which provides different results than those calculated using constrained cost-benefit analysis. Here we focus on the areas where IAMs can be deployed to asses uncertainty and risk management, learning, and regional heterogeneity in climate change impacts.

This paper is a revised version of: http://ssrn.com/abstract=2643293.The paper derives the optimal carbon tax in closed-form from an integrated assessment of climate change. The formula shows how carbon, temperature, and economic dynamics quantify the optimal mitigation effort. The model's descriptive power is comparable to numeric models used in policy advising. Uncertainty surrounding climate change remains large, and the paper derives closed-form expressions of welfare loss from shocks and epistemological uncertainty. These expressions interact (intrinsic) risk attitude, distributional moments, and the climatic shadow values, and they exhibit different sensitivities to time preference. Welfare gains from reducing uncertainty about temperature feedbacks are much higher than the gains from better measurements of carbon flows.