Richard Sweeney (10/06/21)

Boston College

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“Winds of Change: Technical Progress and Learning in Wind Power” (with Thom Covert)

Abstract: We study the impact of learning-by-doing and product innovation on the path of technical progress in wind turbine manufacturing. To measure changes in wind turbine cost over time, we leverage a simple but physically realistic model of how observable wind turbine characteristics, like rotor size, relate to power production and manufacturing material needs.  We embed this into a model of wind turbine demand, and estimate it using 20 years of global data on wind farm characteristics, including wind speeds, power prices, and the turbines they installed.  Our cost estimates negatively correlate both with manufacturer experience, and with measures of manufacturing innovation, like the delivery of newer and larger turbines.  These results are consistent with a theory that LBD and innovation explain much of the observed decline in wind turbine prices over the last 20 years.

Zoom: https://berkeley.zoom.us/j/96401179551

David Lobell (04/22/20)

David Lobell

Stanford University

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“Are satellite data ready for agricultural economists? ”

Abstract: Satellites have been observing the Earth for many decades, with monitoring of agriculture a key application since the beginning. Yet the generation of quantitative datasets has been lacking, hampering the ability of econometricians to achieve their divine purpose of causal inference, especially in developing countries where existing agricultural datasets are limited. Recent advances both in sensors and algorithms have been improving the scope for tracking key agricultural outcomes. This talk will review these advances, with particular emphasis on (i) how yield estimates from satellite compare to traditional ground measures, and (ii) examples of how these estimates can be applied to quantify associations and assess causality in both developed and developing country settings. It will also review remaining obstacles to make satellite-based datsasets truly mainstream for use by econometricians.

Sam Kortum (04/24/20)

Sam Kortum

Yale University
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“Optimal Unilateral Carbon Policy”

Abstract: We consider climate policy by one country in a world with international trade in energy and in manufactured goods produced with energy and labor. Assuming that the country’s trading partner is passive, we derive an optimal unilateral policy to confront the global externality from manufacturing’s combustion of carbon-based energy. Our solution strategy combines techniques from Markusen (1975) and from Costinot, Donaldson, Vogel, and Werning (2015). We interpret the optimal policy as a particular set of taxes and subsidies. The key features are: (i) the country institutes a carbon tax equal to its damages from emissions, raising the cost of energy for its manufacturers relative to the price received by its energy extractors; (ii) this Pigouvian tax is the sum of an extraction tax and a production tax on the use of energy, with a border adjustment on imports equal to the production tax; (iii) the mix between the extraction and production tax is optimized to reduce carbon leakage and to improve the country’s terms of trade; (iv) the border adjustment on the energy content of imported manufactured goods leaves the country’s consumption decisions undistorted; (v) energy taxes are not removed at the border for exports, but instead the country subsidizes exports of goods in which its comparative advantage is weak while taxing those where its comparative advantage is strong; (vi) the country expands exports of manufactures on the extensive margin, potentially even exporting goods that it also imports. Features (i)-(iii) of this optimal policy are reminiscent of Markusen, in a model with trade only in energy. Features (iv)-(v) are reminiscent of Costinot et al., in a model with no externalities. A novel property, captured in (vi), is how the country can exploit international trade in manufactured goods to expand the reach of its climate policy. Through its tax policy the country indirectly controls how energy is used in producing both its imports and its exports.

This seminar will be online. Zoom details will be provided a couple of days prior.

Clare Balboni (03/11/20)

Clare Balboni

Massachusetts Institute of Technology
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“In Harm’s Way? Infrastructure Investments and the Persistence of Coastal Cities”

Abstract: Coasts contain a disproportionate share of the world’s population, reflecting historical advantages, but environmental change threatens a reversal of coastal fortune in the coming decades as natural disasters intensify and sea levels rise. This paper considers whether large infrastructure investments should continue to favour coastal areas. I use a dynamic spatial equilibrium framework and detailed georeferenced data from Vietnam to examine this issue and find evidence that coastal favouritism has significant costs. Road investments concentrated in coastal regions between 2000 and 2010 had positive returns but would have been outperformed by al-locations concentrated further inland even in the absence of sea level rise. Future inundation renders the status quo significantly less efficient. Under a central sea level rise scenario, welfare gains 72% higher could have been achieved by a foresighted allocation avoiding the most vulnerable regions. The results highlight the importance of accounting for the dynamic effects of environmental change in deciding where to allocate infrastructure today. Full Paper

Lint Barrage (12/04/19)

Lint Barrage

UC Santa Barbara
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“Climate Change, Directed Innovation, and Energy Transition: The Long-run Consequences of the Shale Gas Revolution”

Abstract: We investigate the short- and long-term effects of a shale gas boom in an economy where energy can be produced with coal, natural gas, or clean energy sources. In the short run, cheaper natural gas has counteracting effects on CO2 emissions: on the one hand it allows substitution away from coal which reduces CO2 emissions, ceteris paribus; on the other hand the shale gas boom may increase pollution as it increases the scale of aggregate production. We then empirically document another potentially important effect, namely that the shale boom was associated with a decline in innovation in green relative to fossil fuels-based electricity generation technologies. Introducing directed technical change dynamics in our model, we derive conditions under which a shale gas boom reduces emissions in the short-run but increases emissions in the long-run by inducing firms to direct innovation away from clean towards fossil fuels innovation. We further show the possibility of an infinitely delayed switch from fossil fuels to clean energy as a result of the boom. Finally, we present a quantitative version of the model calibrated to the U.S. economy, and analyze the implications of the shale boom for optimal climate policy.

Steve Cicala (11/06/19)

Steve Cicala

University of Chicago
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“Advantageous Selection as a Policy Instrument: Unraveling Climate Change”

Abstract: This paper applies principles of advantageous selection to overcome obstacles that prevent the implementation of Pigouvian policies to internalize externalities. Focusing on negative externalities from production (such as pollution), we evaluate settings in which aggregate emissions are known, but individual contributions are unobserved by the government. The government provides firms with the option to pay a tax on their voluntarily and verifiably disclosed emissions, or an output tax based on the average of rate of emissions among the undisclosed firms. The certification of relatively clean firms raises the output-based tax, setting off a process of unraveling in favor of disclosure. We derive the conditions under which unraveling will yield an outcome close to the first best. We then implement our mechanism in an international setting with unilateral climate change policy as the motivation. We show how such a mechanism extends the reach of a carbon tax, and that the gains over a system of carbon tariffs depend on a small number of estimable parameters. Full Paper

Geoffrey Heal (11/20/19)

Geoffrey Heal

Columbia Business School
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“Coase, Hotelling and Pigou: The Incidence of a Carbon Tax and CO2 Emissions”

Abstract: A carbon tax has been widely discussed as a way of reducing fossil fuel use and mitigating climate change, generally in a static framework. Unlike standard goods that can be produced, oil is an exhaustible resource. Parts of its price reflects scarcity rents, i.e., the fact that there is limited availability. We highlight important dynamic aspects of a global carbon tax, which will reallocate consumption through time: some of the initial reduction in consumption will be offset through higher consumption later on. Only reserves with high enough extraction cost will be priced out of the market. Using data from a large proprietary database of field-level oil data, we show that carbon prices even as high as 200 dollars per ton of CO2 will only reduce cumulative emissions from oil by 4% as the supply curve is very steep for high oil prices and few reserves drop out. The supply curve flattens out for lower price, and the effect of an increased carbon tax becomes larger. For example, a carbon price of 600 dollars would reduce cumulative emissions by 60%. On the flip side, a global cap and trade system that limits global extraction by a modest amount like 4% expropriates a large fraction of scarcity rents and would imply a high permit price of $200. The tax incidence varies over time: initially, about 75% of the carbon price will be passed on to consumers, but this share declines through time and even becomes negative as oil prices will drop in future years relative to a case of no carbon tax. The net present value of producer and consumer surplus decrease by roughly equal amounts, which are almost entirely offset by increased tax revenues. Full Paper

Katherine Meckel (09/18/19)

Katherine Meckel

UC San Diego
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“Are Inspections Going to Waste? Using Machine Learning to Improve EPA Inspection Targeting of Hazardous Waste Facilities”

Abstract: Machine learning (ML) algorithms are increasingly used to model and predict economic outcomes. Using 15 years of data and nearly 10,000 variables, we build an ML model to predict the likelihood that manufacturing facilities will violate EPA regulations on hazardous waste. Given that the EPA can inspect a limited number of these facilities per year, we simulate the case in which the EPA’s inspection choices are replaced by facilities predicted to be high risk by our model. The results suggest that our model’s predictions improve on the EPA’s rate of finding violations by 50%. To validate our estimates of the model’s efficacy at improving targeting, we run a multi-year field test in which the EPA and the model each choose half of the facilities to be inspected. A field test that incorporates real world implementation challenges is critical for agency adoption. Ours is the first direct test of potential for machine learning to improve on the decision based targeting of government resources in the U.S.