Dynamic optimisation in the presence of threshold effects when the location of the threshold is uncertain – with an application to a possible disintegration of the Western Antarctic Ice Sheet

Nævdal, Eric

doi:10.1016/j.jedc.2005.04.004

Cited by 69 publications

(46 citation statements)

References 23 publications

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“…Following Naevdal (2003Naevdal ( , 2006, we assume the location of the thresholdT is distributed according to the function h(T ) with support [0, ∞]. If a value T > 0 is attained without reaching the threshold, then it must be true thatT > T .…”

Section: C2 Unknown Threshold Locationmentioning

confidence: 99%

“…The problem for the regulator is given by: Following Naevdal (2003Naevdal ( , 2006 and Naevdal and Openheimer (2007), the risk- which is a decreasing function of m and g. Equation (C.34) captures the benefits from implementing SGE sooner. As in the deterministic case, the first term captures the direct effect, which is equivalent to the discounted reduction in future mitigation and SGE implementations.…”

Section: C2 Unknown Threshold Locationmentioning

confidence: 99%

“…The use of SGE as an insurance against catastrophic climate change has been proposed early in the literature (Keith (2000), Victor (2008), Keith et al (2010), Moreno-Cruz and Keith (2013)). The idea of SGE as a complement to mitigation is proposed in the literature as a way to achieve any given 1 In the appendix we present an alternative model, which more closely follows Naevdal (2003), Naevdal (2006), and Naevdal and Oppenheimer (2007).…”

Section: Introductionmentioning

confidence: 99%

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Climate tipping points and solar geoengineering

Heutel

Moreno‐Cruz

Shayegh

2016

Journal of Economic Behavior & Organization

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We study optimal climate policy when climate tipping points and solar geoengineering are present. Solar geoengineering reduces temperatures without reducing greenhouse gas emissions. Climate tipping points are irreversible and uncertain events that cause large damages. We analyze three different rules related to the availability of solar geoengineering: a ban, using solar geoengineering as insurance against the risk of tipping points, or using solar geoengineering only as remediation in the aftermath of a tipping point. We model three distinct types of tipping points: two that alter the climate system and one that yields a direct economic cost. Using an analytic model, we find that an optimal policy, which minimizes expected losses from the tipping point, includes both emissions reductions and solar geoengineering from the onset.Using a numerical simulation model, we quantify optimal policy and various outcomes under the alternative scenarios. The presence of tipping points leads to more mitigation and more solar geoengineering use and lower temperatures. * We thank Derek Lemoine and seminar participants at Tennessee, Stanford, Harvard, and the Southern Economic Association annual meeting for comments.

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Section: C2 Unknown Threshold Locationmentioning

confidence: 99%

Section: C2 Unknown Threshold Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Climate tipping points and solar geoengineering

Heutel

Moreno‐Cruz

Shayegh

2016

Journal of Economic Behavior & Organization

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“…Accounting for possible shifts, different types of uncertainty may play a role. For example, the shift can be triggered when some a-priori unknown threshold is crossed (as in Tsur and Zemel, 1996;Naevdal, 2006). Alternatively, it may be due to a random occurrence controlled by some given hazard rate (Clarke and Reed, 1994;Tsur and Zemel, 1998;Gjerde et al, 1999;Haurie and Moresino, 2006).…”

Section: Introductionmentioning

confidence: 99%

Regime shifts and uncertainty in pollution control

Zeeuw

Zemel

2012

Journal of Economic Dynamics and Control

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 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 uncertainty, are compared with the ambiguous outcomes reported in the literature for similar models. The differences are explained in terms of the properties of the damage function associated with each model. The framework is used to analyze the adaptation vs. mitigation dilemma and provides a simple criterion to determine whether adaptation activities should be undertaken promptly, delayed to some future date, or avoided altogether. Terms of use: Documents in EconStor mayJEL-Code: Q280, Q520, Q540.

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“…Mäler et al (2007) apply a similar model to generate a shadow price for resilience, where resilience influences the probability of regime shift. A slightly different modeling approach to thresholds was taken by Naevdal (2001Naevdal ( , 2003Naevdal ( , 2006 who does not include the potential for shocks so that the probability of regime shift is positive only in time periods when stocks are being depleted. Zemel (2006, 2008) (2003) and Mäler et al (2003) analyze a model with a convex-concave regeneration function that captures the potential for regime shifts.…”

Section: Introductionmentioning

confidence: 99%

Optimal Management with Potential Regime Shifts

2010

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We analyze how the threat of a potential future regime shift affects optimal management. We use a simple general growth model to analyze four cases that involve combinations of stock collapse versus changes in system dynamics, and exogenous versus endogenous probabilities of regime shift. Prior work has focused on stock collapse with endogenous probabilities and reaches ambiguous conclusions about the effect of potential regime shift on optimal management. We show that all other cases yield unambiguous results. In particular, with endogenous probability of regime shift that affects system dynamics the potential for regime shift causes optimal management to become precautionary. JEL-codes: E61, O40, Q20, C61

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Dynamic optimisation in the presence of threshold effects when the location of the threshold is uncertain – with an application to a possible disintegration of the Western Antarctic Ice Sheet

Cited by 69 publications

References 23 publications

Climate tipping points and solar geoengineering

Climate tipping points and solar geoengineering

Regime shifts and uncertainty in pollution control

Optimal Management with Potential Regime Shifts

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