Optimal Control Models and Elicitation of Attitudes towards Climate Damages

Abstract

forthcoming in Environmental Modeling and Assessment special issue on "Modeling the economic response to global climate change" Abstract: This paper examines the consequences of various attitudes towards climate damages through a family of stochastic optimal control models (RESPONSE): cost-efficiency for a given temperature ceiling; costbenefit analysis with a "pure preference for current climate regime" and full cost-benefit analysis. The choice of a given proxy of climate change risks is actually more than a technical option. It is essentially motivated by the degree of distrust regarding the legitimacy of an assessment of climate damages and the possibility of providing in due time reliable and non controversial estimates. Our results demonstrate that a) for early decades abatement, the difference between various decision-making frameworks appears to matter less than the difference between stochastic and non stochastic approach given the cascade of uncertainty from emissions to damages; b) in a stochastic approach, the possibility of non-catastrophic singularities in the damage function is sufficient to significantly increase earlier optimal abatements; c) a window of opportunity for action exists up to 2040: abatements further delayed may induce significant regret in case of bad news about climate response or singularities in damages.

Comparison of physically- and economically-based CO<sub>2</sub>-equivalences for methane

“…As noted earlier, there is no consensus on the shape of the damage function and an exponent function was chosen for simplicity. Other shapes have been proposed, such as a hockey stick (Tol et al, 1998), an S-shaped or sigmoid function (Ambrosi et al, 2003) or a sum of sigmoid functions. Hockey-stick damage functions may be appropriate in costbenefit analysis (they essentially imply that there is a threshold GMST change, above which we should mitigate at any cost), but would cause some inconsistency in the GDP calculation as the temperature change trajectory is prescribed independently from the damage function.…”

Toward a Sustainable and Resilient Future

“…Social and collective learning includes support for joint problem solving, power sharing, and iterative reflection (Berkes, 2009). The need to take into account the arrival of new information in the design of response strategies has also been mentioned for mitigation policies (Ha Duong et al, 1997;Ambrosi et al, 2003). Adaptive management is an incremental and iterative learning-by-doing process, whereby participants make sense of system changes, engage in actions, and finally reflect on changes and actions.…”

Optimal growth with adaptation to climate change

“…This model cannot be solved analytically. The optimal growth model with climate change adaptation and no uncertainty resulting is still inspired by the classical Ramsey/Cass/Koopmans model, as well as from the DICE (Nordhaus, 1994) and RESPONSE (Ambrosi et al, 2003) integrated assessment models. The innovation is that we introduce protection capital in addition to the productive capital as outlined in section 2.…”

“…The other factor is an exogenous energy efficiency improvement; this setup is used in Nordhaus (1994), for example. The carbon cycle and temperature equations are the same as in Nordhaus and Boyer (2000), with the calibration of the temperature cycle following Ambrosi et al (2003) (not shown here).…”