Comparison of integrated assessment models: Carbon price impacts on U.S. energy

Abstract

Integrated assessment models (IAMs) are increasingly used to evaluate carbon policy impacts on energy structure, but different models can yield considerably different results. This paper seeks to frame model results for policymakers and other consumers of model outputs. In this analysis we compare three models: GCAM, MERGE, and EPPA. We apply diagnostic carbon price scenarios, such as ramps and shocks, to identify key differences in model behavior that drive inter-model variability in projected policy impacts on the U.S. energy system. We report model results using several economic parameterizations and find that variation in carbon emissions across models results primarily from differences in carbon intensity of energy supply. These differences arise because models include different low-carbon energy technology options and vary widely in how flexible the electricity supply sector is at adapting to a change in policy.The timing of emissions abatement is also strongly influenced by whether the model is a simulation or an inter-temporal optimization scheme and the amount of foresight exhibited in the model. Our analysis demonstrates the usefulness of novel IAM diagnostic indicators and clarifies model features that are highly relevant for consumers of model results. field. Some recent examples: EMF29 assessed the role of border carbon adjustments in unilateral climate policy (Böhringer et al., 2012); EMF28 compared scenarios for transforming the European energy system (Weyant et al., 2013); EMF24 compared climate policy and technology scenarios for the U.S. (Fawcett et al., 2014); and EMF27 evaluated interactions between climate change policy architectures and advanced energy technology availabilities at global scale (Weyant and Kriegler, 2014). Each of these studies included more than a dozen models and organizations. The Assessment of Climate Change Mitigation Pathways and Evaluation of the Robustness ofMitigation Cost Estimates (AMPERE) project included 22 institutions and 17 models (Kriegler, Riahi, et al., 2014). This three-year study assessed the potential costs of delaying climate policy, technology availability and path dependency of energy systems, and the implications of action in a fragmented international policy landscape. Others have addressed a wide range of timely topics (e.g., EMF, 2004;Weyant et al., 2006Weyant et al., , 2013Clarke et al., 2009;Fawcett et al., 2009). Large inter-model comparisons, such as EMF and AMPERE, typically harmonize the models by setting, at a minimum, the values of population and GDP to be similar (e.g., EMF, 2011;Kriegler, 2013).Depending on the focus of the study, other parameters can also be harmonized. This allows different models and methods to be more easily compared. Such model comparison efforts have made model behaviors and features more accessible to the community at large. These inter-model comparison studies are very useful for comparing model responses, but the models and scenarios generally remain under the control of the modelers. Some organizations have mad...

The comparative importance for optimal climate policy of discounting, inequalities and catastrophes

“…8 This suggests a value of ω less than 1 for a carbon tax alone with no compensatory measures. Several other studies (Cullenward et al 2014;Metcalf 2009;Sterner 2012;Williams et al 2014;Wilkerson et al 2015) not only agree with the studies just cited but also conclude that if an increase in energy prices is combined with compensatory measures, it need not disproportionately hit the poor and could even make all but the highest quintile net beneficiaries-for example, if the compensatory measures involve equal per capita redistribution of the revenues from a carbon tax. 9 In light of this, we consider that a relevant range for ω is from 0 to 2, the latter value being obtained when the cost is borne more heavily by the rich.…”

“…They confirm that Schelling's Reversal can occur and that this depends in an intuitive way on the distribution of damages and mitigation costs within regions: the more the poor stand to benefit from 11 Note that for some values of these elasticities, increasing η results in lower carbon prices than the price at η = 0 (which is the same across panels), and for other values, increasing η results in greater prices. 12 If the mitigation costs were distributed in a way that made all but the richest quintile net beneficiaries (Metcalf 2009;Sterner 2012;Wilkerson et al 2015;Williams et al 2014), the results would presumably be even starker. mitigation and the less they pay for it, the more likely the reversal.…”

Leakage risks of geologic CO2 storage and the impacts on the global energy system and climate change mitigation

“…The GCAM projections of the effects of leakage are expected to be greater than from other IAMs because inter-model comparison studies have shown that GCAM tends to deploy CCS more than other IAMs (van der Zwaan et al 2013;Wilkerson et al 2015). The increase in CO 2 storage costs from the worst-case leakage cases considered here leads to at most a small reduction in the deployment of CCS, which is negligible compared with the inter-model discrepancies.…”

Decomposing TIAM-MACRO to Assess Climatic Change Mitigation

“…Our work is focusing on integrated energy-economy models with a comprehensive representation of the energy system. The interested reader is recommended to consult also recent publications [1,2] discussing integrated assessment models for climate policy analysis and a comprehensive review of relatively recent developments in the field of integrated assessment models [3].…”

“…Although the authors give some pedagogic examples of their approach, such generalized formulations linking top-down with bottom-up models are not yet developed. Nevertheless, quite a few efforts are undertaken to integrate, based on soft linking methods, TIMES with general equilibrium models like, e.g., GEMINI-E3 [8], GEM-E3 [9], in order to capture sectoral impacts in terms of welfare and labor losses as function of stringent environmental policies 1 .…”