Climate change policy under polar amplification

Abstract

Polar amplification is an established scientific fact which has been associated with the surface albedo feedback and to heat and moisture transport from the Equator to the Poles. In this paper we unify a two-box climate model, which allows for heat and moisture transport from the southern region to the northern region, with an economic model of welfare optimization. Our main contribution is to show that by ignoring spatial heat and moisture transport and the resulting polar amplification, the regulator may overestimate or underestimate the tax on GHG emissions. The direction of bias depending on the relations between marginal damages from temperature increase in each region. We also determine the welfare cost when a regulator mistakenly ignores polar amplification. Finally we show the adjustments necessary to the market discount rate due to transport phenomena as well as how our two-box model can be extended to Ramsey-type optimal growth models. Numerical simulations confirm our theoretical results.

As Bad as it Gets: How Climate Damage Functions Affect Growth and the Social Cost of Carbon

“…2 These authors present estimations of damage functions parameters for 140 countries and regions and for six climate impacts: sea level rise, variation in crop yields, heat effects on labor productivity, human health, tourism and household energy demand showing the heterogeneity of the different damages. 3 Technological change is addressed in Acemoglu et al (2012), the impact of risk in Bretschger and Vinogradova (2016), the spatial distribution in Brock and Xepapadeas (2017), the time distrubution in Gerlagh and Liski (2017) and Bretschger and Karydas (2017) and the North-South aspects in Bretschger and Suphaphiphat (2014).reducing both current income and economic growth; for a long-run ecological phenomenon economic dynamics become crucial.…”

Climate engineering under deep uncertainty

“…Then the increase in radiative forcing, which induces an increase in the global mean temperature, is determined by the well-known relationship between the current concentration of CO 2 in the atmosphere and the pre-industrial concentration in 1750 (see Nordhaus, 2014 , pp.16-17). 3 In this paper, for tractability reasons, we employ a further simplification of the climate model (see for example Hassler et al, 2016 , Section 3.2.6, or Brock andXepapadeas, 2017;Brock and Hansen, 2017 ), which is based on climate literature developed over the last decade ( Matthews et al, 2009;Pierrehumbert, 2014 ). The simplification is based on linking emissions of CO 2 directly to changes in global mean temperature through the carbon-climate response (CCR), instead of linking CO 2 emissions to CO 2 concentration through carbon sensitivity and CO 2 concentration to changes in global mean temperature through climate sensitivity.…”

As Bad as it Gets: How Climate Damage Functions Affect Growth and the Social Cost of Carbon

“…He modifies the RICE/DICE setup by adding a term to the damage function where temperature is raised to the power of 6.76. As a result, damages turn out to be 50 percent of output at 9 Golosov et al (2012) adopt a slightly different functional form for D by assuming exponential damages but this approximates the other function well. 10 Even the higher estimate of Hanemann (2008) is not compatible with the 2 • C target of international climate policy which implicitly assumes that damages of temperature rise exceeding 2…”

“…Specifically, a quartic damage function raises the social cost of carbon by more than a factor of ten compared to the linear function. 4 Technological change is addressed in Acemoglu et al (2012), the impact of risk in Bretschger and Vinogradova (2016), the spatial distribution in Brock and Xepapadeas (2017), the time distrubution in Gerlagh and Liski (2017) and Bretschger and Karydas (2017) and the North-South aspects in Bretschger and Suphaphiphat (2014).…”

“…The higher the damage, the lower the coefficient and thus output and income. 9 The functional form of the damage function and its parametrization has been inspired by numerical estimations of predicted climate damages. Nordhaus (2007) estimates that damages are 1.7 percent of output at 2.5 • C warming, which provides a parameter value for the quadratic term; Hanemann (2008) presents estimates for the same temperature which are 2.4 times as much, so a different value appears warranted.…”