Environmental and Energy Economics
March 26-27, 2015
Wayne B. Gray, Clark University and NBER, and Ann Ferris, Ronald Shadbegian, and Ann Wolverton, Environmental Protection Agency,
State-level renewable portfolio standards (RPSs) are a relatively recent but increasingly popular phenomena in the United States, enacted to encourage cleaner production of electric power. To date there has not been any research on how the adoption of an RPS affects manufacturing activity. In this paper, Ferris, Gray, Shadbegian, and Wolverton estimate the impact RPS requirements have on U.S. manufacturing activity, and in particular, labor demand and production, via their effect on electricity prices faced by manufacturing facilities. Authors begin their analysis by modelling the adoption of state-level RPS requirements. Next, using a plant-level dataset for the entire U.S. manufacturing sector from 1990 - 2009, they model how the stringency of RPS requirements impact the electricity prices faced by manufacturing plants and then, in turn, how electricity prices affect manufacturing activity both in general and in energy-intensive, trade-exposed (EITE) sectors. The estimated effects of an RPS on electricity prices and manufacturing employment and output are small. For example, authors find that electricity prices faced by plants that purchase electricity subject to an RPS that requires utilities to generate 3% of their electricity from renewable sources are approximately 3-6% higher than the electricity price faced by plants purchasing from a non-RPS utility. For the plants in EITE industries Ferris, Gray, Shadbegian, and Wolverton estimate that a 6% increase in electricity prices would cause employment, production hours and output to decrease by approximately 2-3%.
Lucas W. Davis, University of California, Berkeley and NBER, and Gilbert E. Metcalf, Tufts University and NBER
Information provision is a key element of government energy-efficiency policy, but the information that is provided is often too coarse to allow consumers to make efficient decisions. An important example is the ubiquitous yellow "EnergyGuide" label, which is required by law to be displayed on all major appliances sold in the United States. These labels report energy cost information based on average national usage and energy prices. Davis and Metcalf conduct an online stated-choice experiment to measure the potential welfare benefits from labels tailored to each household's state of residence. The researchers find that state-specific labels lead to significantly better choices. Consumers choose to invest about the same amount overall in energy-efficiency, but the allocation is much better with more investment in high-usage high-price states and less investment in low-usage low-price states. The implied aggregate cost savings are larger than the cost of implementing state-specific labels.
Garth Heutel, Georgia State University and NBER; Juan Moreno Cruz, Georgia Institute of Technology; and Soheil Shayegh, Carnegie Institution of Washington
Heutel, Moreno-Cruz, and Shayegh consider the socially optimal use of geoengineering as a tool to manage climate change. Geoengineering offers the possibility of reducing the damages from atmospheric greenhouse gas concentrations, potentially at a lower cost than reducing emissions. If so, then an optimal policy path can include less abatement than is recommended by models that do not include geoengineering, and the price of carbon will be lower. Solar geoengineering reduces temperature but does not reduce atmospheric or ocean carbon concentrations, and that carbon may cause damages apart from temperature rise. Finally, uncertainty about both climate change and about geoengineering affects the optimal deployment of geoengineering. The researchers explore these issues with both an analytical model and a numerical simulation. The optimal carbon tax is lower than the tax recommended by the model without geoengineering, substantially so depending on the parameterizations of geoengineering costs and benefits. Carbon concentrations are higher but temperature changes are lower when allowing for geoengineering. All policy paths are sensitive to calibrated parameter values, and the optimal level of geoengineering is more sensitive to climate uncertainty than is the optimal level of abatement. The point estimates should be interpreted with caution since there is a great deal of uncertainty surrounding feasibility and side effects of geoengineering.
Stephen P. Holland of University of North Carolina, Greensboro and NBER; Erin T. Mansur, Dartmouth College and NBER; Nicholas Muller, Middlebury College and NBER; and Andrew J. Yates of University of North Carolina, Chapel Hill,
Electric vehicles offer the promise of reduced environmental externalities relative to their gasoline counterparts. Holland, Mansur, Muller, and Yates determine the spatial heterogeneity in these externalities and evaluate several spatially-differentiated policies to correct them. To do this, the researchers combine a discrete-choice model of new vehicle purchases, an econometric analysis of the electric power industry, and the AP2 air pollution model. They find three main insights. First, there is considerable spatial variation in the environmental benefit of electric cars, ranging from a positive $3025 in California to a negative $4773 in North Dakota. Second, the vast majority of environmental externalities from driving an electric car in one place are exported to other places, implying that electric cars may be subsidized locally, even though they may lead to negative environmental benefits overall. Third, spatially differentiated policies can raise welfare, but the effect is much stronger for taxes on miles driven than for subsidies on vehicle purchases.
Klaus Desmet, Southern Methodist University; David Nagy, Princeton University; and Esteban Rossi-Hansberg, Princeton University and NBER
Desmet, Nagy, and Rossi-Hansberg study the relationship between geography and growth. To do so, the researchers first develop a dynamic spatial growth theory with realistic geography. They characterize the model and its balanced growth path and propose a methodology to analyze equilibria with different levels of migration frictions. The authors bring the model to the data for the whole world economy at a 1° x 1° geographic resolution. They then use the model to quantify the gains from relaxing migration restrictions as well as to describe the evolution of the distribution of economic activity in the different migration scenarios. The results indicate that fully liberalizing migration would increase welfare more than three-fold and would significantly affect the evolution of particular regions in the world. The researchers then use the model to study the effect of a spatial shock. They focus on the example of a rise in the sea level and find that coastal flooding can have an important impact on welfare by changing the geographic-dynamic path of the world economy.
Marco Gonzalez-Navarro and Matthew Turner, University of Toronto
Gonzalez-Navarro and Turner investigate the relationship between the extent of a city's subway network, its population and its spatial configuration. To accomplish this investigation, for a sample of the 632 largest cities in the world, the researchers construct panel data describing the extent of each of the 138 subway systems in these cities, their population, and measures of centralization calculated from lights at night data. These data indicate that large cities are more likely to have subways, but that subways have at most a small effect on urban population growth. Consistent with economic theory and with other studies of the effects of transportation improvements on cities, their data also indicate that subways cause cities to be more decentralized.
Christopher Timmins, Duke University and NBER, and Ashley Vissing, Duke University
With the growth of shale gas in the U.S., lease negotiations have become an important part of the energy landscape. Royalty payments are a potential source of benefit to homeowners and restrictions negotiated directly in leases are an important tool by which the industry is regulated. Using a unique combination of data sets, Timmins and Vissing adapt the dual-gradient hedonic model to measure the capitalization of lease clauses into housing values. Results provide a measure of the benefits to homeowners from the regulations negotiated in leases, and suggest that factors affecting the outcomes of lease negotiations will have pecuniary impacts on homeowners.
Christiane J.S. Baumeister, Bank of Canada, and Lutz Kilian, University of Michigan
Futures markets are a potentially valuable source of information about market expectations. Exploiting this information has proved difficult in practice, because the presence of a time-varying risk premium often renders the futures price a poor measure of the market expectation of the price of the underlying asset. Even though the expectation in principle may be recovered by adjusting the futures price by the estimated risk premium, a common problem in applied work is that there are as many measures of market expectations as there are estimates of the risk premium. Baumeister and Kilian propose a general solution to this problem that allows them to uniquely pin down the best possible estimate of the market expectation for any set of risk premium estimates. The researchers illustrate this approach by solving the long-standing problem of how to recover the market expectation of the price of crude oil. They provide a new measure of oil price expectations that is considerably more accurate than the alternatives and more economically plausible. The authors discuss implications of their analysis for the estimation of economic models of energy-intensive durables, for the debate on speculation in oil markets, and for oil price forecasting.
Mark R. Jacobsen, University of California, San Diego and NBER; Christopher R. Knittel, MIT and NBER; James M. Sallee, University of Chicago and NBER; and Arthur van Benthem, University of Pennsylvania and NBER
Many of the most important policies that aim to reduce greenhouse gas emissions and other environmental externalities do so by regulating the energy efficiency ratings of energy-consuming durable goods. However, each product's lifetime externality depends not only on these ratings, but also on its lifetime utilization. As a result, conventional energy efficiency policies are inefficient when products differ significantly in their average longevity. Jacobsen, Knittel, Sallee, and van Benthem develop a theoretical model that characterizes this inefficiency using sufficient statistics that require minimal market data. The researchers then explore the quantitative importance of this phenomenon for the case of automobiles using data on lifetime vehicle mileage from a large sample of automobiles. They document substantial heterogeneity in the longevity of different types of cars, and their model translates this heterogeneity into welfare implications. The authors estimate that fuel economy standards that regulate fuel economy but ignore longevity are able to recover only about one-third of the welfare gains achievable by a policy that also takes longevity into account.