The Center will develop structural models of the global environment (including climate change and the economy) that can be used to design optimal decision making. For this purpose, structural models are necessary to investigate some issues such as regulation and optimal policy. In the current environment, in which extreme shocks can have a severe impact on the functioning of the economy, nonlinear models are particularly difficult to solve and usually require the use of sophisticated solution methods. The quantitative policy recommendations that the center targets rely on three technical pillars: a complex economic model, data, and computational algorithms. The Center aims at being a leader in computational economics and finance and high-performance computing and providing the cutting-edge computational infrastructure required to address these questions.

Pareto-Improving Carbon-Risk Taxation

Laurence J. Kotlikoff, Felix Kubler, Andrey Polbin, Simon Scheidegger

Economic Policy, 2021

Anthropomorphic climate change produces two conceptually distinct negative economic externalities. The first is an expected path of climate damage. The second, which is this paper’s focus, is an expected path of economic risk. To isolate the climate-risk problem, we consider mean-zero, symmetric shocks in our 12-period, overlapping generations model. These shocks impact dirty energy usage (carbon emissions), the relationship between carbon concentration and temperature, and the connection between temperature and damages. Our model exhibits a de minimis climate problem absent its shocks. But due to non-linearities, symmetric shocks deliver negatively skewed impacts, including the potential for climate disasters. As we show, Pareto-improving carbon taxation can dramatically lower climate risk, in general, and disaster risk, in particular. The associated climate-risk tax, which is focused exclusively on limiting climate risk, can be as large or larger than the carbon average-damage tax, which is focused exclusively on limiting average damage.

Making Carbon Taxation a Generational Win Win

Laurence J. Kotlikoff, Felix Kubler, Andrey Polbin, Jeffrey D. Sachs, Simon Scheidegger

International Economic Review, 62(1), 2021

Abstract: Carbon taxation has been studied primarily in social planner or infinitely lived agent models, which trade off the welfare of future and current generations. Such frameworks obscure the potential for carbon taxation to produce a generational win-win. This paper develops a large-scale, dynamic 55-period, OLG model to calculate the carbon tax policy delivering the highest uniform welfare gain to all generations. The OLG framework, with its selfish generations, seems far more natural for studying climate damage. Our model features coal, oil, and gas, each extracted subject to increasing costs, a clean energy sector, technical and demographic change, and Nordhaus (2017)’s temperature/damage functions. Our model’s optimal uniform welfare increasing (UWI) carbon tax starts at $30 tax, rises annually at 1.5 percent and raises the welfare of all current and future generations by 0.73 percent on a consumption-equivalent basis. Sharing efficiency gains evenly requires, however, taxing future generations by as much as 8.1 percent and subsidizing early generations by as much as 1.2 percent of lifetime consumption. Without such redistribution (the Nordhaus “optimum”), the carbon tax constitutes a win-lose policy with current generations experiencing an up to 0.84 percent welfare loss and future generations experiencing an up to 7.54 percent welfare gain. With a six-times larger damage function, the optimal UWI initial carbon tax is $70, again rising annually at 1.5 percent. This policy raises all generations’ welfare by almost 5 percent. However, doing so requires levying taxes on and giving transfers to future and current generations ranging up to 50.1 percent and 10.3 percent of their lifetime consumption. Delaying carbon policy, for 20 years, reduces efficiency gains roughly in half.