Conference Agenda

The paper develops an endogenous growth model to characterize decarbonization and its support through effective public policies. Knowledge varies across economic sectors and is accumulated through innovations at the aggregate level and through learning from the conversion of polluting and clean energy sources. Consumption development throughout decarbonization is derived in a closed-form solution. Energy technology undergoes a tipping point at a critical threshold, which is influenced by knowledge diffusion and policy interventions. Carbon taxes and subsidies for clean energy infrastructure have a symmetrical impact on technology tipping. Timely decarbonization by mid-century can only be achieved if either stringent policies are consistently implemented or policies are significantly aligned with progress in clean technologies.

We model energy transitions (from petroleum to shale oil, and from shale oil to renewables) as a sequence of regime-switching problems in general equilibrium using a three-sector model of macroeconomic growth. We fully characterize both transitions analytically and express the optimal switching times in terms of observable statistics such as known reserves, in-situ and ex-situ energy requirements, and indices of net energy production. While the decision to exploit shale oil delays per se the adoption of renewables, the energy requirements of the primary sector in the first two regimes affect substantially macroeconomic performance, the timing of the switch to renewables, and the welfare outcomes of the overall energy transition. Holding total extraction costs fixed, moderate increases (reductions) in the share of ex-situ (in-situ) costs delay the transition to renewables as much as large increases in fossil-fuel reserves -- e.g., massive discoveries of new oil fields -- because reduced energy availability for final producers harms both consumption and investment possibilities before the adoption of renewables. This result appears particularly relevant for the immediate future since the existing technologies for shale-oil extraction typically exhibit higher (overall as well as ex-situ) energy requirements than conventional oil extraction.

This paper delves into repercussions of contagion effects for optimal public policy. We develop a novel dynamic stochastic framework, where disaster arrivals are modeled via the Hawkes process which possesses a self-excitation mechanism. We derive analytical solutions showing that the optimal policy consists of devoting a stochastic fraction of output to disaster-mitigation. The mitigation propensity is an increasing function of the Hawkes intensity and essentially tracks disaster arrivals implying that the policy is reactive. This result is in contrast with the existing literature, which does not take into account the possibility of contagion and therefore finds a constant mitigation propensity to be optimal.

Transversality is an important sufficient condition for identifying the solution in infinite horizon economic and financial models. Without such a condition, there exists a continuum of functions that satisfy the Hamilton-Jacobi-Bellman (HJB) functional equation. In this paper, we explore this manifold of solutions with numerical and analytical methods. Using a standard continuous-time model, we demonstrate that, without explicitly imposing the transversality condition, widely used numerical algorithms, including the (Deep) Galerkin-type methods, may converge to arbitrary points of this manifold, leading to significant and uncontrollable biases. Using an example of the AK-Ramsey model with logarithmic utility (a prototypical model for many environmental economics and financial mathematics applications), the paper demonstrates

that the area of direct applicability of projection-type algorithms is narrower than one might expect based on contemporary literature.

We propose a novel approach using a functional transformation of the original HJB equation to effectively incorporate the transversality condition, ensuring convergence to the actual value function.