With the rapid expansion of Artificial Intelligence, there are expectations for a proportional expansion of economic activity due to increased productivity, and with it energy consumption and its associated environmental consequences like carbon dioxide emissions. Here, we combine data on...

The purpose of this paper is to extend the dynamic resource allocation problem by including stock externalities like accumulated CO2 and SO2 emissions as well as flow externalities like waste of energy or pollutants which can be abated (SO2). The objective is to examine how the evolution of...

We develop a spatial model of energy exploitation where energy sources are differentiated by their geographic location and energy density. The spatial setting creates a scaling law that magnifies the importance of differences across energy sources. As a result, renewable sources twice as dense,...

The Dasgupta-Heal-Solow-Stiglitz model of capital accumulation and resource depletion poses the following sustainability problem: is it feasible to sustain indefinitely a level of consumption that is bounded away from zero? We provide a complete technological characterization of the...

We develop a simple model of managing a system subject to pollution damage under risk of an abrupt and random jump in the damage coefficient. The model allows the full dynamic characterization of the optimal emission policies under uncertainty. The results, that imply prudent behavior due to...

We show that the standard Value Function Iteration (VFI) algorithm has difficulties approximating models with jump discontinuities in policy functions. We find that VFI fails to accurately identify the location and size of jump discontinuities while other methods - such as the Endogenous Grid...

We analyze optimal compensation schedules for the directors of two plants belonging to the same owner and producing the same good but serving geographically differentiated markets. Since the outcome of each director depends on his own effort and on a random variable representing market...

We propose a new method for solving high-dimensional dynamic programming problems and recursive competitive equilibria with a large (but finite) number of heterogeneous agents using deep learning. The "curse of dimensionality" is avoided due to four complementary techniques: (1) exploiting...