2002
DOI: 10.1007/bf03354027
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Costs of greenhouse gas abatement: meta-analysis of post-SRES mitigation scenarios

Abstract: Economic analyses have produced widely differing estimates of the economic implications of policies for greenhouse gas (GHG) mitigation, ranging from high costs to modest benefits. The main reason for the differences appears to be differences in approaches and assumptions. This paper analyzes the extent to which the post-SRES 1 (after the IPCC Special Report on Emissions Scenarios) model results for the global costs of GHG mitigation can be explained by the model's characteristics and the assumptions adopted. … Show more

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Cited by 34 publications
(30 citation statements)
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“…and there a few recent meta-analyses as well (Barker et al 2002;Fischer and Morgenstern 2006;Kuik et al 2009;Repetto and Austin 1997). There are two equally important messages from this literature.…”
Section: Impacts Of Emission Reduction: a Surveymentioning
confidence: 99%
“…and there a few recent meta-analyses as well (Barker et al 2002;Fischer and Morgenstern 2006;Kuik et al 2009;Repetto and Austin 1997). There are two equally important messages from this literature.…”
Section: Impacts Of Emission Reduction: a Surveymentioning
confidence: 99%
“…Several previous studies note that the estimated costs of reducing greenhouse gas emissions tend to vary a lot across different models and model runs, e.g., [53,[55][56][57]. The presence of a wide range of cost estimates may undermine policy support for fairly stringent climate policy measures; faced with significant uncertainties of the economic impacts politicians may hesitate whether or not to promote tough measures unless more light can be shed on the true costs of these.…”
Section: Estimates Of Climate Policy Costs: Why Do They Differmentioning
confidence: 99%
“…In such cases, a model for filling in the missing observations needs to be devised. Essentially, we conduct a meta-3 With the exception of POLES analysis (Barker et al 2002;Fischer et al 2006;Kuik et al 2009;Repetto et al 1997) and use the estimated model to impute the missing observations. We propose a simple OLS regression 4 between the (log) of the policy costs and a series of independent variables that include the stringency of the climate target in radiative forcing, model dummies, a delayed participation dummy, and an overshoot dummy.…”
Section: Accounting For the Biasmentioning
confidence: 99%