Dynamics of Capital Investment and Pollution Externalities in Wholesale Electricity Markets

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The field of environmental economics was built on the notion of internalizing into markets the social harm caused by pollution. This dissertation examines the implications of putting that idea into practice in the electric power generation sector, with a particular focus on market structure and short- and long-run industry dynamics.

Environmental policy to mitigate climate change seeks to transform the capital composition of industries for the purpose of reducing carbon dioxide emissions. In deregulated wholesale electricity markets, firms may exercise long-run market power through investment and retirement decisions which affect future wholesale price settlement. In Chapter 1, I develop a dynamic structural model of the Texas electricity market spanning 2003-2019 to analyze how long-run market power exercise and environmental policy for reducing carbon emissions affect the capital composition of the industry over time. I find that market power exercise led to significant early fossil fuel plant retirements over this period, with an attendant decrease in consumer surplus on the order of $1.6 billion annually. Further counterfactual analysis suggests that federal production tax credits for wind power expanded the deployment of wind by approximately 73 percent, but the associated reductions in emissions were more than twice as costly as would have been achieved under a $20-per-ton carbon tax.

In Chapter 2 I delve further into the issue of market structure and long-run dynamics. Economic theory suggests that setting the wholesale price of electricity at the marginal social cost of unmet demand during periods of scarcity results in optimal capacity investment in the presence of perfect competition. I examine the implications of applying this principle in a setting where competition is imperfect, and where the market was structured prior to the introduction of competition (deregulation) and therefore not established through firms’ profit maximizing behavior. I build a stylized model that approximates the effects of a scarcity price mechanism under the hourly demand distribution observed in the Texas wholesale electricity market in 2017. I use this model to demonstrate that the scarcity price mechanism may encourage incumbent firms with large portfolios to retire plants, and that firms with a threshold amount of existing infra-marginal generation capacity will be unwilling to invest in new capacity. I then use a dynamic structural model to demonstrate that the scarcity price mechanism introduced in Texas in 2014 accelerated turnover over the period 2014-2019 by driving greater retirement of capacity in addition to greater investment, relative to a counterfactual scenario in which the scarcity price design was not implemented.

In Chapter 3 I shift my focus from long-run industry dynamics and environmental policy concerning a global pollutant (carbon dioxide) to short-run dynamics and harm from a local pollutant (ground-level ozone). NOx emissions are a precursor to ground-level ozone, a pernicious pollutant that is harmful to human health and ecosystems. Despite decades of regulations including NOx emissions pricing, and a corresponding precipitous decline in NOx emissions, episodic high-ozone events prevent many areas from achieving air quality standards. Theoretically, spatially or temporally differentiated emissions prices could be more cost-effective at reducing such events than a uniform price. To test this prediction, using data from the EPA and NOAA spanning 2001-2019, we use novel empirical strategies to estimate (1) the link between hourly emissions and high-ozone events and (2) hourly marginal abatement costs. The estimates form the basis for simulations that compare uniform and differentiated emissions pricing. Consistent with economic theory, differentiated emissions pricing is substantially more cost effective at reducing high-ozone events, but this advantage depends on the accuracy of the estimated NOx-ozone relationship.