Search

Renewable power generation is the key to decarbonizing the electricity system. Wind power is the fastest-growing renewable source of electricity in the United States. However, expanding wind capacity often faces local opposition, partly due to a perceived visual disamenity from large wind turbines. Here, we provide a US-wide assessment of the externality costs of wind power generation through the visibility impact on property values. To this end, we create a database on wind turbine visibility, combining information on the site and height of each utility-scale turbine having fed power into the U.S. grid, with a high-resolution elevation map to account for the underlying topography of the landscape. Building on hedonic valuation theory, we statistically estimate the impact of wind turbine visibility on home values, informed by data from the majority of home sales in the United States since 1997. We find that on average, wind turbine visibility negatively affects home values in an economically and statistically significant way in close proximity (<5 miles/8 km). However, the effect diminishes over time and in distance and is indistinguishable from zero for larger distances and toward the end of our sample. [ABSTRACT FROM AUTHOR]

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond [Rose S, et al. (2014) Understanding the Social Cost of Carbon: A Technical Assessment]. The empirical literature has shown significant increases in climate-driven impacts on overall consumption, yet has not focused on the cost implications of the increased intensity and frequency of extreme events driving peak demand, which is the highest load observed in a period. We use comprehensive, high-frequency data at the level of load balancing authorities to parameterize the relationship between average or peak electricity demand and temperature for a major economy. Using statistical models, we analyze multiyear data from 166 load balancing authorities in the United States. We couple the estimated temperature response functions for total daily consumption and daily peak load with 20 downscaled global climate models (GCMs) to simulate climate change-driven impacts on both outcomes. We show moderate and heterogeneous changes in consumption, with an average increase of 2.8% by end of century. The results of our peak load simulations, however, suggest significant increases in the intensity and frequency of peak events throughout the United States, assuming today's technology and electricity market fundamentals. As the electricity grid is built to endure maximum load, our findings have significant implications for the construction of costly peak generating capacity, suggesting additional peak capacity costs of up to 180 billion dollars by the end of the century under business-as-usual. [ABSTRACT FROM AUTHOR]

Heavier vehicles are safer for their own occupants but more hazardous for other vehicles. Simple theory thus suggests that an unregulated vehicle fleet is inefficiently heavy. Using three separate identification strategies we show that, controlling for own-vehicle weight, being hit by a vehicle that is 1000 pounds heavier generates a 40–50% increase in fatality risk. These results imply a total accident-related externality that exceeds the estimated social cost of US carbon emissions and is equivalent to a gas tax of $0.97 per gallon ($136 billion annually). We consider two policies for internalizing this external cost, a weight-varying mileage tax and a gas tax, and find that they are similar for most vehicles. The findings suggest that European gas taxes may be much closer to optimal levels than the US gas tax. [ABSTRACT FROM PUBLISHER]

A recent paper in this journal argues that the choice of statistical model is responsible for the divergence in damage estimates of climate change on US agriculture. We provide five arguments why we believe this assertion is misguided. [ABSTRACT FROM AUTHOR]

This paper studies the impacts of state level residential building codes on per capita residential electricity consumption. We construct a timeline of when individual states first implemented residential building codes. Using panel data for 48 US states from 1970-2006, we exploit the temporal and spatial variation of building code implementation and issuance of building permits to identify the effect of the regulation on residential electricity consumption. Controlling for the effect of prices, income, and weather, we show that states that adopted building codes followed by a significant amount of new construction have experienced detectable decreases in per capita residential electricity consumption--ranging from 0.3-5% in the year 2006. Estimates are larger in states where codes are more stringent and more strictly enforced. [ABSTRACT FROM AUTHOR]

This paper examines whether US gasoline content regulations, which impose substantial costs on consumers, have successfully reduced ozone pollution. We take advantage of spatial and temporal variation in the regulations' implementation to show that federal gasoline standards, which allow refiners flexibility in choosing a compliance mechanism, did not improve air quality. This outcome occurred because minimizing the cost of compliance does not reduce emissions of those compounds most prone to forming ozone. In California, however, we find that precisely targeted, inflexible regulations requiring the removal of particularly harmful compounds significantly improved air quality. (JEL L51, L71, L78, Q53, Q58) [ABSTRACT FROM AUTHOR]

This paper provides comparisons of a variety of time-series methods for short-run forecasts of the main greenhouse gas, carbon dioxide, for the United States, using a recently released state-level data set from 1960–2001. We test the out-of-sample performance of univariate and multivariate forecasting models by aggregating state-level forecasts versus forecasting the aggregate directly. We find evidence that forecasting the disaggregate series and accounting for spatial effects drastically improves forecasting performance under root mean squared forecast error loss. Based on the in-sample observations we attempt to explain the emergence of voluntary efforts by states to reduce greenhouse gas emissions. We find evidence that states with decreasing per capita emissions and a “greener” median voter are more likely to push toward voluntary cutbacks in emissions. [ABSTRACT FROM AUTHOR]

Using fine-scale panel data and an econometric model, we predict land use change in the Midwestern United States if a new bioenergy crop, Miscanthus × Giganteus (miscanthus), is introduced. To explain farmers' current crop choices, we use a local, limited dependent variable regression based on soil and weather characteristics. To this model, we add miscanthus as a new crop, based on its place dependent BioCro model-predicted yield. We find that the vast majority of land used to grow miscanthus will come from land now used for non-major crops, pasture, woodland, and other uses. This implies that miscanthus can help mitigate climate change by displacing oil usage without causing food conflict. [ABSTRACT FROM AUTHOR]

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

Long-haul truck electrification has attracted nascent policy support, but the potential health and climate impacts remain uncertain. Here, we developed an integrated assessment approach with high spatial-temporal (km and hourly) resolution to characterize the causal chain from truck operation to charging loads, electricity grid response, changes in emissions and atmospheric concentrations, and the resulting health and climate impacts across the United States. Compared to future diesel trucks, electrified trucking's net health benefits are concentrated only along the West Coast with a business-as-usual electricity grid. However, with an 80%-renewable electricity grid, most regions would experience net health benefits, and the economic value of avoided climate and health damages exceeds $5 billion annually, an 80% reduction relative to future diesel trucks. Electric trucks with larger batteries may increase health and climate impacts due to additional trips needed to compensate for the payload penalty, but a 2× improvement in the battery specific energy (to ∼320 Wh/kg) could eliminate the additional trips.