Climate policy/Data uncertainties limitations: Difference between revisions

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{{ComponentDataUncertaintyAndLimitationsTemplate
{{ComponentDataUncertaintyAndLimitationsTemplate
|Reference=Enerdata, 2010; Kindermann et al., 2008;  
|Reference=Enerdata, 2010; Kindermann et al., 2008;
|Description=<h2> Data uncertainties and limitations </h2>
|Description=<h2> Data uncertainties and limitations </h2>


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<div class=“version changev31”>
<div class="version changev31">


Each FAIR module has uncertainties. The main uncertainties in the cost modules are future business-as-usual emission trends (higher emission trends imply higher mitigation costs to achieve a certain target) and MAC curves (difficult to estimate the costs of reducing emissions far into the future). In the Global Pathfinder FAIRSiMCaP and Climate module, uncertainty in the climate sensitivity of the climate system to greenhouse gas concentration is an key source of uncertainty but can be covered by using a probabilistic version of the [[MAGICC model|MAGICC]] climate model. Probably the largest source of uncertainty relates to climate change damage, as there are few studies on the economic damage of climate change on a global or regional scale.
Each FAIR module has uncertainties. The main uncertainties in the cost modules are future business-as-usual emission trends (higher emission trends imply higher mitigation costs to achieve a certain target) and MAC curves (difficult to estimate the costs of reducing emissions far into the future). In the Global Pathfinder FAIRSiMCaP and Climate module, uncertainty in the climate sensitivity of the climate system to greenhouse gas concentration is an key source of uncertainty but can be covered by using a probabilistic version of the [[MAGICC model|MAGICC]] climate model. Probably the largest source of uncertainty relates to climate change damage, as there are few studies on the economic damage of climate change on a global or regional scale.

Revision as of 11:24, 18 November 2016