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Studies in Empirical Policy Evaluation: New Methods and Applications to the Energy Transition


The electricity sector is experiencing a period of significant change as policymakers grapple with a range of economic and environmental challenges. Climate change, health, equity, efficiency, reliability and safety are all front of mind. Formulating and enacting policy to tackle these challenges and ensure prosperity in an environmentally stable world means making big decisions with limited understanding of how they will play out. A critical requirement for making progress on these pressing issues is the ability to interrogate the decisions that have been made, quantify their effects, and use that knowledge to make better decisions in the future. The research set out here aims to do exactly that, through developing new empirical methods and applying them in novel ways to two key policy areas in energy and environmental economics.

First I study the phase-out of nuclear power in Germany. Many countries have phased out nuclear electricity production in response to concerns about nuclear waste and the risk of nuclear accidents. In joint work with Olivier Deschenes and Akshaya Jha, we examine the impact of the shutdown of roughly half of the nuclear production capacity in Germany after the Fukushima accident in 2011. We use hourly data on power plant operations and a novel machine learning framework to estimate how plants would have operated differently if the phase-out had not occurred. We find that the lost nuclear electricity production due to the phase-out was replaced primarily by coal-fired production and net electricity imports. The social cost of this shift from nuclear to coal is approximately 3 billion euros per year. The majority of this cost comes from the increased mortality risk associated with exposure to the local air pollution emitted when burning fossil fuels. Even using alternative assumptions regarding the value of avoided health damages and the impact of the phase-out on the deployment of renewable power, the social costs still range from 1 to 8 billion per year. It is challenging to find estimates of the benefits from reduced nuclear operating costs, accident risks and waste disposal that can outweigh social costs of this magnitude.

Second, I study the deployment of renewable energy in the United Kingdom. Large infrastructure projects can create widespread societal benefits and are often critical to tackling major national or global challenges. However, they also frequently prompt strong opposition from local residents and businesses. This is sometimes pejoratively labeled NIMBY (Not In My Backyard) behavior, and while it is thought to be common in many settings the economic costs it imposes are poorly understood. In this paper I estimate the economic costs of so-called NIMBYism. To do this I examine the case of renewable energy in the United Kingdom, where I draw on detailed planning data for all renewable energy projects spanning three decades, including projects that were proposed but not approved. I first use hedonic methods to estimate how the construction of a wind or solar project is capitalized into local property values. I find that wind projects have significant negative local impacts whilst solar projects do not. I then quantify the weight that planning officials place on various factors during the planning process and find evidence that they are indeed particularly responsive to local impacts. The result has been a systematic refusal of societally beneficial projects. Ultimately misallocated investment due to the planning process may have increased the cost of the UK's deployment of wind power by 10-25%. A significant portion of this can plausibly be attributed to NIMBYism.

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