
Instrument Choice, Carbon Emissions, and Information
Author(s) -
Michael W. Wara
Publication year - 2015
Publication title -
michigan journal of environmental and administrative law/michigan journal of environmental and administration law
Language(s) - English
Resource type - Journals
eISSN - 2375-6284
pISSN - 2375-6276
DOI - 10.36640/mjeal.4.2.instrument
Subject(s) - emissions trading , carbon tax , pollution , baseline (sea) , odds , economics , air pollution , greenhouse gas , environmental economics , information asymmetry , pollutant , natural resource economics , environmental science , microeconomics , computer science , ecology , oceanography , logistic regression , chemistry , organic chemistry , machine learning , biology , geology
This Article examines the consequences of a previously unrecognized difference between pollutant cap-and-trade schemes and pollution taxes. Implementation of cap-and-trade relies on a forecast of future emissions, while implementation of a pollution tax does not. Realistic policy designs using either regulatory instrument almost always involve a phase-in over time to avoid economic disruption. Cap-and-trade accomplishes this phase-in via a limit on emissions that falls gradually below the forecast of future pollutant emissions. Emissions taxation accomplishes the same via a gradually increasing levy on pollution. Because of the administrative complexity of establishing an emissions trading market, cap-and-trade programs typically require between three and five years lead time before imposing obligations on emitters. In this Article, I present new evidence showing that forecast error over this timeframe for United States energy- related carbon dioxide emissions from the Department of Energy’s energy model—the model used for policy design by Congress and EPA—is biased and imprecise to such a degree as to make its use impractical. The forecasted emissions are insufficiently accurate to allow for creation of a reliable or predictable market signal to incentivize emission reductions. By contrast, carbon taxes, because they do not depend upon a baseline emissions forecast, create a relatively clear level of policy stringency. This difference matters because policies that end up weaker than intended face low odds for strengthening, while those that end up stronger than intended are likely to be weakened. The political asymmetry combined with actual model forecast errors leads to bias in favor of suboptimal, weak, policies for cap-and-trade. This is a serious concern if, as is usually the case, a cap is set based on political bargaining rather than on an optimal balancing of abatement costs and avoided climate damage. By contrast, the same model bias would lead to more environmentally effective than forecast carbon taxes but without the political consequences created by price volatility, were such programs to be implemented in the United States. Thus, while theory tells us that cap-and-trade and carbon taxes can be equivalent, imperfect information leads to suboptimal environmental performance of emissions trading, relative to carbon taxation policies. Policymakers should weigh these practical, information-related concerns when considering approaches to controlling emissions of greenhouse gases.