Evaluating the Cost-Effectiveness of Domestic Policy Portfolios for Emissions Abatement in the Electricity Sector
Abstract
Increased scientific certainty about anthropogenic climate change coupled with the
Obama administration taking office has the United States poised to enact legislation
to reduce greenhouse gas emissions. Studies have suggested that a combination of
policy instruments will be more economically efficient than any single strategy in
order to correct for multiple externalities in the market. This paper employs a theoretical
model of the electricity sector developed in Fischer & Newell (2008) to compare the
cost-effectiveness of individual and combinations of policy instruments. Despite
many of the parameter values changing significantly in the numerical application of
the model, this analysis affirms the relative rankings of the policy instruments in
terms of economic efficiency found in Fischer & Newell (2008). Beginning with the
most cost-effective instrument, the order is as follows: emissions price, tradable
emissions performance standard, tax on fossil output, renewables portfolio standard,
renewables production subsidy, and R&D subsidy. Several policy portfolios are modeled
and costs compared to an emissions price. The results indicate that simultaneous
policies for climate mitigation tend to be more cost-effective than any single instrument.
While combining an emissions price with a renewables portfolio standard offers cost
advantages over an emissions price alone, the best complementary policy for an emissions
price is an R&D subsidy. The sensitivity analyses show that this is a robust conclusion
for a broad range of parameter values. It is therefore recommended that policymakers
seriously consider a substantial R&D subsidy for renewable energy technologies in
addition to establishing a cap-and-trade system.
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https://hdl.handle.net/10161/1009Citation
Wanner, Brent (2009). Evaluating the Cost-Effectiveness of Domestic Policy Portfolios for Emissions Abatement
in the Electricity Sector. Master's project, Duke University. Retrieved from https://hdl.handle.net/10161/1009.Collections
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