Thomas S. Lontzek

DSICE: A Dynamic Stochastic Integrated Model of Climate and Economy

This paper introduces a dynamic stochastic integrated model of climate and economy (DSICE), and a numerical dynamic programming algorithm for its solution. More specifically, we solve an example with annual time periods, a six hundred year horizon, and shocks to the economic and climate system. Our dynamic programming methods solve such models on a laptop in about an hour, and do so with good accuracy. This decisively refutes the pessimism one often hears about the possibility of solving such models.

Environmental tipping points significantly affect the cost−benefit assessment of climate policies

Significance Most current cost−benefit analyses of climate change suggest global climate policy should be relatively weak. However, relatively few studies account for the market or nonmarket impacts of passing environmental tipping points that cause abrupt and irreversible damages. We use a stochastic dynamic model of the climate and economy to quantify the effect of tipping points on climate change policy. We show that environmental tipping points can profoundly alter cost−benefit analysis, justifying a much more stringent climate policy, which takes the form of a higher immediate price on carbon. Most current cost−benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost−benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost−benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost−benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.

Tipping Points in a Dynamic Stochastic IAM

We use a dynamic stochastic general equilibrium model of integrated climate and economy (DSICE) to account for abrupt and irreversible climate change. We model a climate shock in the form of a stochastic tipping point. We investigate the impact of the tipping point externality on optimal mitigation policy.We conclude that the optimal mitigation policy depends on the dynamic pattern of the impact. In the case of abrupt and irreversible climate change with a permanent impact, the optimal policy implies a constant anti-tipping effort to prevent the catastrophe, calling for immediate limitations on emissions.

Risk‐Averse Mitigation Decisions in an Unpredictable Climate System

Risk aversion plays a central role in the decisions made in the face of uncertainties, and climate-change mitigation should be no exception. However, the interlinkage of risk aversion and climate-change uncertainties has hardly been investigated numerically, in part because of the computational difficulties of stochastic optimization. In this paper, we apply the numerical techniques of stochastic optimization to the economic modeling of climate change, with the aim of modeling the decision preferences of a risk-conscious agent in the face of unpredictable climate change. The model underlines the critical role played by the risk-aversion parameter in determining the effects of uncertainties on mitigation, not only in level but also in sign.

A NONLINEAR PROGRAMMING METHOD FOR DYNAMIC PROGRAMMING

A nonlinear programming formulation is introduced to solve infinite horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to avoid inefficient discretization. Our numerical results show that this nonlinear programming method is efficient and accurate.