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There is great uncertainty about future climate conditions and the appropriate policies for managing interactions between the climate and the economy. We develop a multidimensional computational model to examine how uncertainties and risks in the economic and climate systems affect the social cost of carbon (SCC)---that is, the present value of the marginal damage to economic output caused by carbon emissions. The SCC is substantially increased by economic and climate risks at both current and future times. Furthermore, the SCC is itself a stochastic process with significant variation; for example, the basic elements of risk incorporated into our model cause the SCC in 2100 to be, with significant probability, ten times what it would be without those risks. We have only imprecise information about what parameter values are best for approximating reality. To deal with this parametric uncertainty we perform extensive uncertainty quantification and show that these findings are robust for a wide range of alternative specifications. More generally, this work shows that large-scale computing can enable economists to examine substantially more complex and realistic models for the purposes of policy analysis.

The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.