Abstract
Cost-Effectiveness Analysis (CEA) determines climate policies that reach a given climate target at minimum welfare losses. However, when applied to temperature targets under climate sensitivity uncertainty, decision-makers might be confronted with normatively unappealing negative expected values of future climate information or even infeasible solutions. To tackle these issues, Cost-Risk Analysis (CRA), that trades-off the costs for mitigating climate change against the risk of exceeding climate targets, has been proposed as an extension of CEA under uncertainty. Here we build on this proposition and develop an axiomatically sound CRA for the context of uncertainty and future learning. The main contributions of this paper are: (i) we show, that a risk-penalty function has to be non-concave to avoid counter-intuitive preferences, (ii) we introduce a universally applicable calibration of the cost-risk trade-off, and (iii) we implement the first application of CRA to a numerical integrated assessment model. We find that for a 2°-target in combination with a 66 % compliance level, the expected value of information in 2015 vs. 2075 is between 0.15 % and 0.66 % of consumption every year, and can reduce expected mitigation costs by about one third. (iv) Finally, we find that the relative importance of the economic over the risk-related contribution increases with the target probability of compliance.
Highlights
Integrated Assessment Models (IAMs) are formal representations of the interconnected socio-economic and climate systems
This study addresses the fraction within the climate economics community that currently focuses on Cost-Effectiveness Analysis (CEA) as opposed to Cost-Benefit Analysis (CBA)
Stricter targets and earlier learning increase the value of information in most cases, we showcase circumstances under which stricter targets can decrease the value of information. (iv) we show that the value of information consists of economic and risk-related value and that the composition depends on the climate target
Summary
Integrated Assessment Models (IAMs) are formal representations of the interconnected socio-economic and climate systems. Such a negative value is normatively unappealing and does not enable the above mentioned necessity of gauging the value of further climate observations In resolving both conceptual problems of CEA/CCP at once, Schmidt et al (2011) proposed “Cost-Risk Analysis” (CRA) in which a trade-off is made between the risk of overshooting climate target and the economic cost of switching to renewable energy. Thereby, they provide a normative decision criterion that does not explicitly require a climate damage function, but builds on a consensus-based climate target. Online Resource 1 includes background calculations and further substantiating figures
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