Marginal Abatement Costs and Marginal Welfare Costs for Greenhouse Gas Emissions Reductions: Results from the EPPA Model

By Jennifer Morris, S. Paltsev, J. Reilly. Environmental Modelling and Assessment, 2011

214 citations recorded by the source corpus.

Marginal abatement cost (MAC) curves, relationships between tonnes of emissions abated and the CO2 (or greenhouse gas (GHG)) price, have been widely used as pedagogic devices to illustrate simple economic concepts such as the benefits of emissions trading. They have also been used to produce reduced-form models to examine situations where solving the more complex model underlying the MAC is difficult. Some important issues arise in such applications: (1) Are MAC relationships independent of what happens in other regions?, (2) are MACs stable through time regardless of what policies have been implemented in the past?, and (3) can one approximate welfare costs from MACs? This paper explores the basic characteristics of MAC and marginal welfare cost (MWC) curves, deriving them using the MIT Emissions Prediction and Policy Analysis model. We find that, depending on the method used to construct them, MACs are affected by policies abroad. They are also dependent on policies in place in the past and depend on whether they are CO2-only or include all GHGs. Further, we find that MACs are, in general, not closely related to MWCs and therefore should not be used to derive estimates of welfare change. We also show that, as commonly constructed, MACs may be unreliable in replicating results of the parent model when used to simulate GHG policies. This is especially true if the policy simulations differ from the conditions under which the MACs were simulated.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/fb7fda40180621b70ae7989fea9f4a7f2d7ba1ad

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In Economics and Econometrics

  1. Limin Du
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  3. Chu Wei
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In Renewable Energy, Sustainability and the Environment

  1. Limin Du
  2. Chu Wei
  3. Aoife Hanley
  4. Paul Ekins
  5. Fabian Kesicki
  6. F. Polzin

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